Apparatuses, methods and systems for insight discovery and presentation from structured and unstructured data

ABSTRACT

The APPARATUSES, METHODS AND SYSTEMS FOR INSIGHT DISCOVERY AND PRESENTATION FROM STRUCTURED AND UNSTRUCTURED DATA (“IDAP”) provides a platform that, in various embodiments, is configurable to identify, display, and act upon insights derived from large volumes of data. In one embodiment, the IDAP is configurable to determine values and relationships for primal data. Identified relationships may be leveraged to build queries optimized for efficient data access across data volumes. The IDAP may also be configured to identify drivers of global metrics of interest, leverage those drivers to discern the efficacy of media and/or advertising campaigns, and provide recommendations to improve and/or optimize campaign efficacy.

PRIORITY CLAIM

This application is a non-provisional of, and claims priority under 35U.S.C. § 119, to prior U.S. provisional patent application Ser. No.62/089,232 entitled, “APPARATUSES, METHODS AND SYSTEMS FOR INSIGHTDISCOVERY AND PRESENTATION FROM STRUCTURED AND UNSTRUCTURED DATA,” filedDec. 9, 2014, and prior U.S. provisional patent application Ser. No.62/134,470 entitled, “APPARATUSES, METHODS AND SYSTEMS FOR INSIGHTDISCOVERY AND PRESENTATION FROM STRUCTURED AND UNSTRUCTURED DATA,” filedMar. 17, 2015.

This application is also a continuation-in-part, and claims priorityunder 35 U.S.C. § 120, to prior U.S. non-provisional patent applicationSer. No. 14/929,246 entitled, “APPARATUSES, METHODS AND SYSTEMS FOREFFICIENT AD-HOC QUERYING OF DISTRIBUTED DATA,” filed Oct. 30, 2015,which in turn is a non-provisional of, and claims priority under 35U.S.C. § 119, to prior U.S. provisional patent application Ser. No.62/072,923 entitled, “APPARATUSES, METHODS AND SYSTEMS FOR EFFICIENTAD-HOC QUERYING OF DISTRIBUTED DATA,” filed Oct. 30, 2014.

All of the aforementioned applications are expressly incorporated intheir entirety herein by reference.

FIELD

The present innovations generally address efficient data collection,storage, and evaluation, and more particularly, include APPARATUSES,METHODS AND SYSTEMS FOR INSIGHT DISCOVERY AND PRESENTATION FROMSTRUCTURED AND UNSTRUCTURED DATA.

BACKGROUND

The advent of the internet and mobile device technologies have broughtabout a sea change in the distribution and availability of information.Ubiquitous electronic communications have resulted in large volumes ofinformation being generated and, often, made widely available.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying appendices and/or drawings illustrate variousnon-limiting, example, innovative aspects in accordance with the presentdescriptions:

FIG. 1 shows an implementation of data flow for data compacting in oneembodiment of IDAP operation;

FIG. 2A shows an implementation of data structure for compacted data inone embodiment;

FIG. 2B shows an implementation of data flow for query processing in oneembodiment of IDAP operation;

FIG. 3 shows an example of logic flow for pack file generation in oneembodiment of IDAP operation;

FIG. 4 shows an example of logic flow for master count file generationin one embodiment of IDAP operation;

FIG. 5 shows an example of logic flow for map generation and use in oneembodiment of IDAP operation;

FIGS. 6A-6D show examples of logic flow for query processing withcompact term search phrases in embodiments of IDAP operation;

FIG. 7 shows aspects of logic flow for an embodiment of insightdiscovery and presentation;

FIG. 8 shows aspects of exploration mode logic flow for a topic builderin one embodiment;

FIG. 9 shows aspects of evaluation mode logic flow for a topic builderin one embodiment;

FIG. 10 shows aspects of logic flow for social media influence discoveryin one embodiment;

FIGS. 11A-11D show a table of primals and related insights in oneembodiment;

FIGS. 12A-12I show aspects of user interface in embodiments of IDAPoperation; and

FIG. 13 shows a block diagram illustrating embodiments of a IDAPcontroller.

The leading number of each reference number within the drawingsindicates the figure in which that reference number is introduced and/ordetailed. As such, a detailed discussion of reference number 101 wouldbe found and/or introduced in FIG. 1. Reference number 201 is introducedin FIG. 2, etc.

DETAILED DESCRIPTION IDAP

The APPARATUSES, METHODS AND SYSTEMS FOR INSIGHT DISCOVERY ANDPRESENTATION FROM STRUCTURED AND UNSTRUCTURED DATA (“IDAP”) provide aplatform that, in various embodiments, is configurable to identify,display, and act upon insights derived from large volumes of data. Inone embodiment, the IDAP is configurable to determine values andrelationships for primal data, which is used to construct naturallanguage insights. Insight records may be automatically filtered and/orsorted according to various selectable criteria. In some embodiments,insight records may be employed to generate natural language reports,generate action-item reports, and/or implement recommended actions suchas advertisement purchases. In various embodiments, the IDAP may beconfigurable as a topic builder, permitting users to discovery topics,tags, labels, and/or the like to assign to documents in a corpus and/orto facilitate highly optimized queries over volumes of data. In otherembodiments, the IDAP may be configurable for influence discovery acrosssocial media and/or other structured and/or unstructured documentsources. For example, the IDAP may be configured to identify subsets ofsocial media users responsible for driving one or more global metrics(e.g., sales, subscriptions, and/or the like) from unstructured data,e.g., without requiring exemplars, templates, and/or the like for suchusers in advance. In other embodiments, the IDAP may be configurable toevaluate efficacy and/or return on investment of advertising and/orother media campaigns and/or to recommend actions for improvementthereof.

In one embodiment, IDAP includes a distributed, in-memory, real-timecomputing platform that supports fast ad-hoc querying against largevolumes of data. In one implementation, this comprises an in-memorycombination of map/reduce and faceted search. IDAP may be used, in oneimplementation, for fast slicing and dicing of, for example, social data(e.g., social network post and/or feed data), terms derived therefrom,and/or the like. In one implementation, IDAP apparatuses, methods andsystems may include the following:

convert the data into a compact, tightly packed byte structure accordingto one or more customized schema/protocol (in one implementation, thisreduces the size of the original JSON social joins by up to 72%);

distribute slices (e.g., by an IDAP master server) of the compacted dataamong multiple nodes (e.g., IDAP workers) in the cluster;

perform custom, on-the-fly map/reduce type operations over the compactdata in-memory across all nodes;

in one implementation, execution of queries lazily unpacks only theportions of the compact records that are useful for a particular type ofquery;

in one implementation, the system may cache “facet offsets” into thecompact records to improve performance of queries that refer to aparticular facet.

In one implementation, a Java Virtual Machine application toolkit suchas Akka Cluster may be utilized for distributed communication betweenthe master/worker nodes in the cluster.

In one embodiment, IDAP supports operations over the social data suchas, but not limited to:

counts

time series

sample

top K over entities/favs

statistical “slice” compare

Querying

In one implementation, a check may be performed as to whether thecluster is up, operational, and/or the like. This may be achieved, forexample, with a status call similar to the following example:

-   curl http://IDAP:3000/status

In this example, curl is a Linux command for making HTTP requests on thecommand-line of a system running a Linux-based operating system. Inother implementations, a user could make the HTTP request by, forexample, entering a corresponding uniform resource locator (URL) into aweb browser. In some embodiments, any tool that can make HTTP requestsmay be used as a client interface for RTC operation, includingsubmitting queries and receiving responses.

In one implementation, the status call may also yield a list of allverticals loaded in IDAP.

In one implementation, a time series over the entire vertical (e.g.,counts de-duped by user/day) may be requested, such as via a commandsimilar to the following example:

-   curl http://IDAP:3000/timeseries?targetVertical=haircare

In one implementation, appending “target” to the name of a particularfield (e.g., from “Vertical” to “targetVertical”) may signify narrowingof the search query to a particular element, value, and/or the like forthat field.

In one implementation, total counts over the entire vertical during aparticular date and/or date range (counts de-duped by user over giventime range, or over entire vertical if no time range specified) may berequested, such as via a command similar to the following example:

-   curl http://IDAP:3000/counts?targetVertical=haircare&targetStartDate

In one implementation, a time series over the entire vertical for peopletalking about, for example, “hair” may be requested, such as via acommand similar to the following example:

-   curl    “http://IDAP:3000/timeseries?targetVertical=haircare&targetTopic=.*hair.*”

In one implementation, a sample of haircare tweets for, e.g., ShampooBrand 1 may be requested (more results may be obtained, e.g., byspecifying a higher value via the sampleSize parameter), such as via acommand similar to the following example:

-   curl    “http://IDAP:3000/sample?targetVertical=haircare&targetQpids=Shampoo    Brand 1:22a42”

In one implementation, ranked entities for Shampoo Brand 1 tweetstalking about “hair” (more results may be obtained, e.g., by specifyinga higher value via the numResults parameter) may be requested, such asvia a command similar to the following example:

-   curl    “http://IDAP:3000/compare?targetVertical=haircare&targetEntities=shine&targetQpids=ShampooBrand    1:

In one implementation, the top 50 raw entity counts for the haircarevertical and shampoo topic (more results may be obtained, e.g., byspecifying a higher value via the numResults parameter) may berequested, such as via a command similar to the following example:

-   curl    “http://IDAP:3000/entityCounts?targetVertical=haircare&targetEntities=shampoo”

In one implementation, the date entity matrix for the carrier verticalfor the top K global entities K may be changed, e.g., with thenumResults parameter; in one implementation, this defaults to 5000) maybe requested. In one implementation, this will return a results zip filecontaining the date entity matrix in MatrixMarket format.

-   curl    “http://IDAP:3000/entityCounts?targetVertical=carrier&groupBy=date”

In one implementation, the above request may be run for one or moretargetQpids, such as according to the following example.

-   curl    “http://IDAP:3000/entityCounts?targetVertical=carrier&targetQpids=att:5a458&groupBy=date”

In one implementation, the top 50 raw fav counts for the haircarevertical and shampoo topic may be requested (you can get more results byspecify a higher value via the numResults parameter), such as via acommand similar to the following example:

-   curl    “http://IDAP:3000/favCounts?targetVertical=haircare?targetEntities=shampoo”

In one implementation, a request may be made to IDAP for all qpidsbelonging, for example, to a particular vertical via the qpids call,e.g.:

-   curl “http://IDAP:3000/qpids?targetVertical=carrier”

In one implementation, qpids may refer to one or more productidentifiers and/or product identification codes.

Query Parameters

In one implementation, query params for the target group may include:

-   targetVertical=haircare-   targetTopic=.*shine.*-   targetQpids=Shampoo Brand 1:22a42-   targetIntentful=true/false-   targetExpr=gender:male*age:0to17|18to24*ethnicity:asian//for asian    males under 25-   targetStartDate=2012-05-01-   targetEndDate=2012-06-01-   targetState=ca-tx-   targetEntities=[(shine,shiny),hair]-   targetFavs=abc

Query params for the reference group:

-   refVertical=haircare-   refTopic=.*shine.*-   refQpids=Shampoo Brand 1:22a42-   refIntentful=true/false-   refExpr=gender:male*age:0to17|18to24*ethnicity:asian//for asian    males under 25-   refStartDate=2012-05-01-   refEndDate=2012-06-01.-   refState=ca-tx-   refEntity=bought-   refEntities=[(shine, shiny),hair]-   refFavs=nbc

In one implementation, the query parameters that support multiple valuesmay include:

-   targetQpids/refQpids (e.g. tmobile:3160a-verizon:e77e9-sprint:05f04)-   targetState/refState (e.g. ca, or ca-ga-il for all three states)-   targetEntities/refEntities (e.g. (buy,buys,bought))-   targetFavs/refFavs (e.g. abc or (abc,nbc,fox))-   targetEntities and targetFavs Parameter Format Embodiments-   Single entity example (match given entity):-   targetEntities=hair-   Negation example (does not match given entity, use leading “!” and    surround entity in parens):-   targetEntities=!(hair)-   Or grouping example (matches any one, enclose in “( )”):-   targetEntities=(hair, curls)-   And grouping example (matches every one, enclose in “[ ]”):-   targetEntities=[hair, shine]-   Mix and match examples:-   targetEntities=[hair,(shine,clean),!(head and shoulders)]-   groupBy Parameters (in one implementation, only for entityCounts/)

In one implementation, the date entity matrix for entityCounts query inMatrixMarket format may take a form similar to the following example:

-   groupBy=date-   numResults Parameters

In one implementation, the top K global Entities being considered forgroupBy query may be limited, e.g.:

-   numResults=10000

Age Expression Parameter

In one implementation, the targetExpr and refExpr parameters support atleast the following age buckets (leave param out entirely from URL forno age filter). Note that, in one implementation, multiple values can bespecified using a pipe to separate them in order to create a bucket forthe full range, e.g. for “under 25” you would specifytargetExpr=age:0to17|18to24. In one implementation, must be prefaced byage: Supported age buckets may include:

-   0to17-   18to24-   25to29-   30to34-   35to39-   40to49-   50to99

Ethnicity Parameter

In one implementation, the targetExpr and refExpr parameters support atleast the following values (leave param out entirely from URL for noethnicity filter) (In one implementation, must be prefaced byethnicity):

-   other-   black-   white-   asian-   hispanic

Gender Parameter

In one implementation, the targetExpr and refExpr parameters support atleast the following values (leave param out entirely from URL for nogender filter)(In one implementation, must be prefaced by gender):

-   male-   female

US State Parameter

In one implementation, the targetState and refState parameters supportat least the following values (leave param out entirely from URL for nogeo/state filter)—the state abbreviation can be specified in upper orlower case as well:

-   AL (alabama)-   AK (alaska)-   AZ (arizona)-   AR (arkansas)-   CA (california)-   CO (colorado)-   CT (connecticut)-   DE (delaware)-   DC (district of columbia)-   FL (florida)-   GA (georgia)-   HI (hawaii)-   ID (idaho)-   IL (illinois)-   IN (indiana)-   IA (iowa)-   KS (kansas)-   KY (kentucky)-   LA (louisiana)-   ME (maine)-   MD (maryland)-   MA (massachusetts)-   MI (michigan)-   MN (minnesota)-   MS (mississippi)-   MO (missouri)-   MT (montana)-   NE (nebraska)-   NV (nevada)-   NH (new hampshire)-   NJ (new jersey)-   NM (new mexico)-   NY (new york)-   NC (north carolina)-   ND (north dakota)-   OH (ohio)-   OK (oklahoma)-   OR (oregon)-   PA (pennsylvania)-   RI (rhode island)-   SC (south carolina)-   SD (south dakota)-   TN (tennessee)-   TX (texas)-   UK (united kingdom)-   UT (utah)-   VT (vermont)-   VA (virginia)-   WA (washington)-   WV (west virginia)-   WI (wisconsin)-   WY (wyoming)

Plotting

In one implementation, the /timeseries call supports a format=plotoptional parameter that will return a zoomable chart (based on highcharts) instead of a JSON time series result.

Example

http://IDAP:3000/timeseries?

targetVertical=carrier&targetQpids=att:5a458&format=plot

Multiple Target Expressions in Single Request

In one implementation, a time series or total count for multiple demosmay be requested in a single call to the service. For example, multipletargetExpr params may be specified for each demo group of interest.

For example:

(JSON)

http://IDAP:3000/timeseries?

targetVertical=hardwarestore&targetQpids=homedepot:7f878&targetExpr=gender:male&targetExpr=gender:female&targetExpr=(buy,buys,buying,bought)&format=json

http://IDAP:3000/counts?

targetVertical=hardwarestore&targetQpids=homedepot:7f878&targetExpr=gender:male&targetExpr=gender:female&targetExpr=(buy,buys,buying,bought)&format=json

(Plots)

http://IDAP:3000/timeseries?

targetVertical=hardwarestore&targetQpids=homedepot:7f878&targetExpr=gender:male&targetExpr=genderfemale&targetExpr=(buy,buys,buying,bought)&format=plot

Request Batching

In one implementation, IDAP supports at least request batching for/counts requests. Taking advantage of request batching can greatlyimprove the performance of a query depending on the use case. Forexample, in performing multiple /counts calls, one may batch them alltogether in a single HTTP request by taking advantage of “indexed”parameters. Each unique request may be prefixed with a unique numericidentifier prefix, e.g. [0], [1], [2], etc. Here's an example of asingle batched IDAP request that contains 2 indexed queries:

http://IDAP:3000/counts?[0]targetVertical=restaurant&[0]targetQpids=tacobell:7d8c7&[0]targetExpr=gender:male&[0]targetEntities=(breakfast)&[1]targetVertical=restaurant&[1]targetQpids=tacobell:7d8c7&[1]targetExpr=gender:female&[1]targetEntities=(dinner)

The above request is a single HTTP request that describes two individualIDAP requests. In one implementation, all parameters belonging to aparticular request are indexed with the same number prefix.

In one implementation, using the IDAP Scala client (see below), therequest batching will be performed automatically within the client.

IDAP Scala Client

In one implementation, all IDAP endpoints may be accessed with a nativeScala IDAP client. Sample usage may take a form similar to the followingexample:

-   import com.qf.IDAP._-   import org.joda.time._-   import com.github.nscala_time.time.Imports._-   def time(f:=>Unit): Long={val start=System.currentTimeMillis; f;-   System.currentTimeMillis-   val client=new IDAPClient(“IDAP”, 3000, 3, 50)-   val intentFilter=“(@dietpepsi,@pepsi)”-   val topics=-   Seq(-   “(aspartame)”,-   “(taste)”,-   “(calories)”,-   “(diabetes,obesity)”,-   “(caffeine)”,-   “(caramel)”,-   “(sweet)”,-   “(commercial)”,-   “(flavor)”)″)-   val    topicsWithIntentFilter=topics.map{topic=>s“[$intentFilter,$topic]”}:+-   val demos=-   Seq(-   //the “all” demo-   “1.0”,-   “gender:female”,-   “gender:female*(0.7348*age:0to17+0.4709*age:18to24+1.9957*age:25to29+1.9868*age:30to34+1.483*age:35to39)”-   “gender:male*(0.7348*age:0to17+0.4709*age:18to24+1.9957*age:25to29+1.9868*age:30to34+1.483*age:35to39)”-   “gender:male”,-   “ethnicity:white”,-   “ethnicity:black”,-   “ethnicity:asian”,-   “ethnicity:hispanic”,-   “ethnicity:other”)-   val geos=-   Seq(-   “state:VT|CT|NY|PA|RI|NH|MA|NJ|ME”,-   “state:ND|MN|IA|MI|NE|KS|MO|OH|IN|WI|IL|SD”,-   “state:WA|OR|CA|AK|HI”,-   “state: TN|MS|FL|DE|MD|AL|KY|GA|SC|OK|VA|AR|DC|WV|NC|TX|LA”,-   “state:NV|UT|AZ|MT|CO|NM|ID|WY”)-   val demoWithGeoExpressions=-   for {-   demo←demos-   geo←geos-   }yield s“$demo*$geo”-   //monthly periods starting from Jan 1st. 20xx-   val periods=Stream.iterate(IDAPDates.mkDate(2012, 1,    1))(_+1.month).takeWhile}_-   time(client.call(-   periods.zip(periods.tail).flatMap{case (startDt, endDt)=>-   topicsWithIntentFilter.map{topic=>-   TotalCountsRequest(-   vertical=“beverage”,-   entities=Some(topic),-   qpids=Seq(“dietpepsi:cd497”),-   expressions=demoWithGeoExpressions,-   startDate=Some(startDt),-   endDate=Some(endDt))-   }-   }))-   client.shutdown( )

Running IDAP Locally

Instructions for running the IDAP locally, in one embodiment:

In one implementation, one or more pack files may be loaded, such asaccording to the following:

-   mkdir −p $HOME/data/packedtweets-   scp −r dr1:/mapr/mapr-dev/data/packed/onlinetravelservice    $HOME/data/packedtweets/

In one implementation, all files are obtained (e.g., done.txt)

In some implementations, other verticals may be too big to fully loadlocally, e.g., on a laptop. Loading a larger vertical (e.g., carrier orbeverage) may be accomplished, for example, by using a subset of thepack files. In one implementation, any subset of pack files may be used.In another implementation, any downloaded pack files include at leastall dictionary and user_fav_mappings files, e.g., to facilitateentity/fav-based queries.

The following are instructions for starting a master server and/or oneor more worker client systems in one embodiment: In an sbt console,switch to the localPtc project, and run re-start. This will start up themaster, wait (e.g., 5 seconds) for it to fully start, and then start aworker. After the worker has loaded all of the data, queries may be runagainst the server. The server may be stopped at any time with re-stop.

In one implementation, when downloading the pack files to a differentlocation, that location may be included as an argument to re-start, egre-start—packFileDir/Users/imran/pack, e.g.:

-   localPtc (in build file:/Users/imran/qf/git/qfish/)-   started-   in the background . . .-   packed.PackedTweetLocal.main( )-   packed.PackedTweetLocal$: creating-   INFO packed.PackedTweetLocal$: master created-   INFO packed.PackedTweetLocal$: starting master . . .-   INFO packed.PackedTweetLocal$: waiting for master to be up-   INFO packed.PackedTweetReader$: finished reading dictionary!-   INFO packed.PackedTweetLocal$: starting worker-   11.669] [Cluster.System-akka.actor.default-dispatcher-3]    [akka://ClusterSystem/user/master

In one implementation, a plurality of queries may be run, and thenre-stop may be run to stop it (e.g., hit enter once to get an sbtprompt), e.g.:

-   localPtc>re-stop-   [info] Stopping application localPtc (by killing the forked JVM) . .    .-   localPtc . . . finished with exit code 143-   [success] Total time: 1 s, completed Jun 10, 20xx 8:24:11 AM-   localPtc>

Naming

In one implementation, apparatuses, methods and systems discussed hereinmay be referred to as a “PTC” (Packed Tweet Cluster).

FIG. 1 shows an implementation of data flow for data compacting in oneembodiment of IDAP operation. An input comprising one or more raw datainput records 102 may, in one implementation, comprise raw text records,JSON records, and/or the like with metadata such as, but not limited to,timestamp, username, location, and/or the like (e.g., social mediacomment, other forms of unstructured text). The input comprising one ormore raw data input records 102 may, in one implementation, be passed todownstream components (e.g., Packed Record Writer 105 comprising FieldSelector 107 and Record Compactor 109) to be compacted into binaryformat for use in efficient search and analysis applications,subroutines, data feeds, and/or the like. In one implementation,compacting the records into a binary format as discussed herein reducesthe size, e.g., by approximately 72%. In one implementation, not allfields from the original raw data are preserved; only those associatedwith a domain of interest. For example, in one implementation, the rawrecords may have certain fields selected (e.g., comment identifier, useridentifier, text, timestamp, metadata, and/or the like), such as by aField Selector module 107, which may then be passed to a recordcompactor module 109 for translating into a more optimized bit-packedformat 110, as described in further detail herein. This bit-packed data110 may then, in some implementations, be later read and/or consumed byother parts of the IDAP and/or used as the source data when respondingto incoming queries.

FIG. 2A shows an implementation of data structure for compacted data inone embodiment. A raw data record (e.g., JSON record) may be convertedinto a compact binary format, such as the example illustrated in FIG. 2,via a custom binary protocol which may include one or more optimizationsto compact data more tightly. In one implementation, each record has afixed header along with a set of known fields and/or values (e.g.,certain fields may be 64-bit SIP hashed rather than storing full text).Fields that can be one of a fixed number of values may be represented,in one implementation, by a smaller type (e.g., a Byte, a Short, and/orthe like). In one implementation, user records and comment records maybe packed separately. In one implementation, the compactedrepresentation may include a “tags” field comprising a bit vector ofenabled/disabled flags, with the corresponding raw JSON recordrepresented in a significantly more verbose manner using multipleattributes and/or fields. The illustrated implementation includes atleast: a Header field (2 bytes) 201; a User ID field (8 bytes) 205; aTimestamp field (8 bytes) 210; a Num Text Bytes field (2 bytes) 215; aText Bytes field (Num Text Bytes*1 byte) 220; a Num Terms field (2bytes) 225; a Terms field (Num Terms*8 bytes) 230; and/or additionalfields 235. In one implementation, certain fields may be configured as64-bit SIP hashed, e.g., as an alternative to storing full text. In oneimplementation, fields that are one of N values may be stored in asmaller type (e.g., Byte/Short).

In another implementation, different types of packed records may begenerated, maintained, accessed, analyzed, and/or the like withinembodiments of IDAP operation. For example, in one implementation, theIDAP may include both packed comment records and packed comment records.In an implementation, a packed comment record may be constructed basedon a schema and/or protocol having a form similar to the followingexample:

Header (2 bytes)

Sequence number (2 bytes)

Tags (2 bytes)

Timestamp (8 bytes)

User identifier (8 bytes)

Comment identifier (8 bytes)

US State (1 byte)

Number of terms (2 bytes)

Terms (number of terms*8 bytes)

Plurals bit set (based on number of terms)

Number of qpids (1 byte)

Qpids (number of qpids*2 bytes)

Number of consumer qpids (1 byte)

Consumer qpids (number of consumer qpids*2 bytes)

Number of text characters (2 bytes)

Text characters (number of UTF-8 encoded bytes)

In this example, Qpids may comprise product identification codes. Inanother implementation, a packed user record may be constructed based ona schema and/or protocol having a form similar to the following example:

Header (2 bytes)

Max sequence number (2 bytes)

Gender/male probability (4 bytes)

Gender/female probability (4 bytes)

Ethnicity/white probability (4 bytes)

Ethnicity/black probability (4 bytes)

Ethnicity/hispanic probability (4 bytes)

Ethnicity/asian probability (4 bytes)

Ethnicity/other probability (4 bytes)

Age/under18 probability (4 bytes)

Age/from18to20 probability (4 bytes)

Age/from21to24 probability (4 bytes)

Age/from25to29 probability (4 bytes)

Age/from30to39 probability (4 bytes)

Age/from40to49 probability (4 bytes)

Age/over50 probability (4 bytes)

Geo (1 byte)

Num favs (4 bytes)

Favs (number of favs*8 bytes)

In one implementation, Compact Terms are packed into memory according tothe smallest number of bytes needed to store the compact term integer.Compact term values from 0 to 255 are stored in one byte, values from256 to 65535 are stored in two bytes and values from 65536 to 8388607are stored in three bytes. In one implementation, values over 8388606are assigned the special compact term value 8388607 which is used toindicate an unmatchable term (no match term). In this way, the mostcommon terms are represented by the smallest storage, reducing theaverage memory storage needs for terms.

In one implementation, text filtering may employ efficient commentqueries using both single and multi-term phrases. To support single termqueries, the compact terms are stored in a sorter order, allowing forbinary searching. To support multi-term queries, the original term orderis made available to compare adjacent terms. Therefore, the in-memorycompact is composed of the following four parts:

A three byte header. In one implementation, the first byte of the headeris the total number of terms in the comment text. Up to 255 terms aresupported. Any terms beyond the 255th term are not included andunavailable for matching. The second byte of the header is the number ofsingle byte (compact term values 0-255) terms. The third and final byteof the header is the number of two byte (compact term values 256-65535)terms. The number of three byte (compact term values 65536-8388607)compact terms can be determined by subtracting the sum of the singlebyte term count and the two byte term count from the total term count(3_byte_terms=total_terms−(1_byte_terms+2_byte_terms)).

The sorted compact terms. In one implementation, the next L bytescontains the compact terms in sorted order, where L=(1byte*1_byte_terms)+(2 bytes*2_byte_terms)+(3 bytes*3_byte_terms). Thefirst 1_byte_terms bytes are all of the single byte compact terms inorder from the lowest to highest. The next 2*2_byte_terms bytes are thetwo byte compact terms in order from lowest to highest. Finally, thelast 3*3_byte_terms bytes contains the three byte compact terms fromlowest to highest. Terms that occur more than once in the original textare repeated as adjacent compact terms in the sorted order, one for eachoccurrence of the term in the original text.

The sorted to original order mapping. In one implementation, the nexttotal_terms bytes represents the mapping between the sorted order andthe original order of terms in the comment text. The value of the ithbyte in this sequence of bytes will be the 0-based index of the originalterm position for the ith sorted compact term. The first byte will holdthe original position of the first compact term in the sorted compactterm section. The final byte will hold the original position of the lastcompact term in the sorted compact term section. Together, these bytescreate a way to map from the sorted compact terms to the correspondingoriginal positions.

The original to sorted order mapping. In one implementation, the nexttotal_terms bytes represents the mapping between the original order ofterms in the comment text and the sorted order of compact terms. Thevalue of the ith byte in this sequence of bytes will be the 0-basedindex of the sorted compact term for the ith original position of thecompact term. The first byte will hold the sorted position of the firstcompact term in the original text. The final byte will hold the sortedposition of the last compact term the original text. Together, thesebytes create a way to map from the original order of compact terms tothe sorted order.

FIG. 2B shows an implementation of data flow for query processing in oneembodiment of IDAP operation. In one implementation, packed recordsproduced via a process such as the example shown in FIG. 1 may bedistributed to worker nodes for computing over a portion of those packedrecords. In one implementation, the master node keeps track of IDAPworkers and handles incoming queries. In one implementation, the masternode orchestrates the process of assigning shards of compacted data toIDAP workers and routes requests to the appropriate request handler. Inone implementation, the IDAP master distributes groups of pack files toIDAP workers once they register with the master (via Akka Cluster). Inone implementation, each IDAP worker loads a portion of the compacteddata and builds certain indexes across certain facets of the binaryrecords.

A client system 203 may, for example, submit raw data (e.g., JSONrecords) to an IDAP master server 206 for processing and/or conversioninto packed records, compacted records, pack files, and/or the like(216, 217, 219) for storage and/or processing by one or more IDAP workersystems (208, 211, 214). In one implementation, the master node keepstrack of IDAP workers and handles incoming queries. In oneimplementation, the master node orchestrates the process of assigningshards of compacted data to IDAP workers. Packed records information mayfurther be processed and/or analyzed to yield one or more indexes (221,222, 224) to facilitate retrieval and/or provision of information inresponse to one or more queries, such as may be relayed by the IDAPmaster 206, received from the client system 203, and/or the like. In oneimplementation, each IDAP worker loads a portion of the compacted dataand builds certain indexes across certain facets of the binary records.In one implementation, IDAP workers (208, 211, 214) may be configured toallow building of custom facet indexes while loading pack files,compacted records, and/or the like. For example, a tree map may beconstructed, such as according to TreeMap[Long, Array[Long]], wheretimestamps are used as keys and values are offsets to off-heap recordsoccurring at that time. An example of a routine for use in connectionwith off-heap binary searching may, in one implementation, take a formsimilar to the following:

def binarySearch{       unsafe: Unsafe,       offset: Long,      fromIndex: Int,       toIndex: Int,       searchTerm: Long) : Int= {       var low = fromIndex       var high −= toIndex − 1       varsearch = true       var mid = 0       while (search && low <= high) <      mid = (low + high) >>> 1       val term = unsafe.getLong (offset +(mid << 3))       if (term < searchTerm) low − mid + 1       else if(term > searchTerm) high = mid − 1       else search = false    }    if(search) − (low + 1) else mid }

Offsets may then, in one implementation, be only processed when theysatisfy the applicable date range. In one implementation, data is notmaterialized unless it is needed to satisfy a particular incomingrequest. For example, in one implementation, a full social comment oruser record is no materialized (e.g., pulled into memory), but ratheronly those fields that are needed for a given incoming request. In oneimplementation, binary searches are performed on sorted items inoff-heap memory. In one implementation, raw data records may be receivedfrom a different client system from the one that later submits a query.In one implementation, the raw data records may be received and/orprocessed internally in the IDAP master 206, may be received and/orprocessed at one or more IDAP workers (208, 211, 214). In oneimplementation, a Java Virtual Machine application toolkit, such as AkkaCluster, may be utilized for distributed communication between IDAPmaster 206 and IDAP workers (208, 211, 214).

In one implementation, queries are distributed in a map/reduce approachfrom the IDAP master to each of the IDAP workers. An example of a querythat queries the IDAP for a time series (e.g., data points) for thefirst five days of 2014 against the “automobile” vertical of socialmedia records that contain the term “fast” and the term “car” may take aform, in one embodiment, similar to the following example:

-   http://rtc:3000/timeseries?targetVertical=automobile&targetStartDate=2014-01-01&targetEndDate=2014-01-06&targetTerms=[fast,car]

An example of a response that this query could elicit, in oneembodiment, may take a form similar to the following example:

[ {       “group” : 0,       “groupTs”: [ {          “expr”: “all”,         “ts” : [ {             “date” : “2014-01-01”            “count” : 137.0          }, {             “date” :“2014-01-02”             “count” : 188.0          }, {            “date” : “2014-01-03”             “count” : 212.0         }, {             “date”2014-01-04”             “count” : 175.0         }, {          “   “date” : “2014-01-05”             “count” :168.0          } ]       } ] } ]

FIG. 3 shows an example of logic flow for pack file generation in oneembodiment of IDAP operation. In one implementation, a pack file writingcomponent of the IDAP system may perform pack file writing offline toconsume a raw annotated data set and pack social comments and userrecords into the custom binary protocol/format (pack files), making itmore readily distributable across IDAP worker nodes. During the packfile writing, the set of unique terms and the corresponding termoccurrence counts are collected for all comments, e.g., in a givendomain (vertical). For pack file's writing, during the processing of acomment 301, text is tokenized into terms 305. These terms are hashed,such as into 64-bit integers 310 using the SipHash 2-4 algorithm (Creference implementation here: https://131002.net/siphash/siphash24.c,incorporated in its entirety herein by reference). These hashes arestored in the comments written to the pack files. The counts ofoccurrences of each term are tracks by using a hash map that maps theterm hash to the count value 315. This value is incremented by one foreach occurrence. When the count for a term reaches a low threshold (T1,default 1) 320, the term hash and the term are appended to a dictionaryTSV file corresponding to the pack file 325. At the conclusion of the ofthe pack file writing, when there are no more terms 330 and, in someimplementations, no more comments 335, the term hashes with countsgreater than or equal to the threshold value (T2) are persisted to asecond TSV file (counts file) along with the corresponding count 340. Inone implementation T2=T1. In another implementation, T2>T1. The countTSV may be used for remaining steps.

FIG. 4 shows an example of logic flow for master count file generationin one embodiment of IDAP operation. In one implementation, eachadditional pack file may be prepared and/or collected 401, and adetermination made as to whether all current pack file writing hasconcluded 405. At the conclusion of the writing of all pack files, theset of count files are read into a new hash map that again maps the termhash to the count value 410. When the same term hash occurs in two ormore count files 415, the counts are summed 420. After all of the countfiles are read and accumulated, the entries whose counts are greaterthan or equal to a larger threshold (T2, default 50) 425 are written toa master count TSV file 430. The set of all dictionary files arecombined into a single master dictionary file with duplicate entries orentries whose corresponding count is less than T2 omitted.

In one implementation, for each IDAP worker loading a vertical's packfiles, the term dictionary and count files may be read into memoryand/or stored in two hash maps. The first hash map may, for example, mapthe term has to count (count map) while the second hash map may, forexample, map the term hash to the term (dictionary map).

FIG. 5 shows an example of logic flow for map generation and use in oneembodiment of IDAP operation. In one implementation, the term hashes aresorted into an array (term array) by count descending 501, with tiesidentified 505 and, e.g., resolved arbitrarily 510. In anotherimplementation, ties may be resolved based on other criteria, alphabet,chronology, and/or the like. The index of a term hash in this term arraybecomes the compact term value for that term 515. A map (compact termmap) that maps the term hash to term array index (called compact termfrom now on) is created. The compact term map can be used to map a termhash into a compact term 520. The term array can be used to map acompact term back into its term hash. When combined with the dictionarymap, in one implementation, the term hash can be mapped back to theoriginal term string 525.

FIG. 6A shows an example of logic flow for query processing with compactterm search phrases in one embodiment of IDAP operation. In oneimplementation, compact term search phrases are used to determine if agiven comment's text matches some given search text. The input searchtext 601 is tokenized into terms 605, e.g., using the same mechanismthat was used to tokenize the comments for the given vertical beingsearched. The resulting terms may be converted into a sequence ofcompact terms 610, e.g., using the SipHash 2-4 and compact term map(from part 2). In one implementation, the matching behavior depends onthe number of terms in the search phrase 615.

Single search term. When the search phrase is composed of a single term,a binary search is performed on the region of the sorted compact termsthat matches the storage size of the search phrase's compact term 620.If the compact term is in the single byte range (0-255), the single bytecompact terms are binary searched. If the compact term is in the twobyte range (256-65535), the two byte compact terms are binary searched.If the compact term is in the three byte range (65536-8388607), thethree byte compact terms are binary searched. If any match is found (andthe search is not multi 640) the comment is determined to match thequery 645; otherwise it does not match 635.

Multiple search terms. When the search phrase has more than one term640, the least common term (the highest compact term value) isdetermined. A binary search is performed on the region of the sortedcompact terms that matches the storage size of the search phrase's leastcommon compact term in the manner described in the single search termsection. If no match is found, the comment cannot match the searchphrase. The least common term is used to increase the likelihood ofearly search failure in this step or any steps below. Otherwise If amatch is found, the matching index (j) 650 is used to determine if thephrase match by examining the adjacent terms in both the phrase and theoriginal text.

Using the sorted to original order mapping bytes, the original positionof the matching index (j) may be determined 655. Based on this position,a quick determination can be made 660 to tell whether the beginning ofthe search phrase would fall before the first position or after the lastposition of the original text. In either of these cases, the searchphrase cannot match in this position and the search may continue with arepeated term as described in FIG. 6D below.

Otherwise, for each compact term that comes before the least commonterm, the compact term is compared with the compact term with the samerelative (negative) offset to j in the comment 670. The original tosorted order mapping bytes are used to convert the original commentposition to the sorted order position which contains the actual compactterm value used for comparison 676. The first compact term that does notmatch will indicate that the search phrase cannot match in this position678 and the search may continue with a repeated term as described inFIG. 6D below 679. Otherwise if all compact terms that come before theleast common term match with the corresponding compact terms in theoriginal text, the search continues.

For each compact term that comes after the least common term, thecompact term is compared with the compact term with the same relative(positive) offset to j in the comment 680. The original to sorted ordermapping bytes are used to convert the original comment position 681,e.g., to the sorted order position which contains the actual compactterm value used for comparison. The first compact term that does notmatch will indicate that the search phrase cannot match in this position682 and the search may continue with a repeated term as described inFIG. 6D below 683. Otherwise if all compact terms that come after theleast common term match with the corresponding compact terms in theoriginal text, the match succeeds and the comment is determined to matchthe search phrase 684.

If this point is reached, alternative positions for matches areinvestigated. Positions in the sorted compact terms adjacent to j maycontain other matches for the least common term in the search phrase. Ifany adjacent values in the sorted compact terms have the same value asthe matching compact term (at position j) 685, these adjacent positionsare examined for matches using the facilities discussed in FIGS. 6A-6Cabove 686. If there are no adjacent positions with matching compact termvalues or all adjacent terms with the same compact value fail to matchin FIGS. 6A-6C above, the comment cannot match the search phrase 687.

In one embodiment, this design decreases the storage requirements from2+(8*total_terms) bytes when storing the term hashes to3+(r*total_terms) bytes when using the compact terms where r is anaverage between 3 and 5. Given the frequency bias towards smallerstorage for the most common terms, the values of r is close to 3 inpractice, typically around 3.2. This achieves an approximately 60%reduction in the bytes needed to store the terms. Further, the searchperformance is much faster than a linear scan when single terms used ormultiple terms are used and the least common term does not match anyterm in the majority of comments.

Insight Discovery and Presentation

An IDAP system may be configured, in some embodiments, to receive andanalyze a corpus of data (e.g., documents, forms, feeds, and/or the likestructured and/or unstructured data), extract factors most responsiblefor driving one or more global metrics, distill data about those factorsinto one or more prose statements, and provide those statements fordisplay at a client terminal and/or via a report.

FIG. 7 shows aspects of logic flow for an embodiment of insightdiscovery and presentation. A collection of primals may be identifiedand/or selected 705, such as according to the availability of primaldata in a corpus of documents; client and/or presentation preferencesand/or requirements; and/or the like. Non-limiting examples of primalsmay include topics, demographic groups, competitor groups, time, and/orthe like. In one implementation, a client-specific ontology, comprisinga collection of data relationships and/or connections between terms,topics, primals, and/or the like (e.g., via primary/foreign key fieldsin a relational database configuration), may be provided to the IDAP foruse in further processing.

Collected primals may then be used to generate a plurality of insights710. In one implementation, an insight comprises a relationship betweenprimals and/or primal information. Non-limiting examples of primals andinsights are provided for illustrative purposes in FIGS. 11A-11D. In oneimplementation, a fixed number (e.g., 10-15) of possible insights arepre-configured, and all possible insights are generated using availableprimal information. In another implementation, a subset of all possibleinsights are generated with available primal information, such asaccording to pre-generation criteria, primal data availability, clientpreference, and/or the like. In one implementation, insight data mayinclude and/or be associated with additional information associated withthe insight and/or related insights, with such data being available forpresentation in a drill-down mode (e.g., made available by selection ofthe insight at the client interface). In one implementation, a naturallanguage insight may be generated by populating an insight templateassociated with the insight record with primal data values alsoassociated with the insight record. For example, primal data valuesassociated with a topic (e.g., “customer service”), a demographic (e.g.,“males age 18-25”), and a brand (e.g., “Soda Brand 1”) may all populatea template, along with the primal trend data (e.g., “less important”) togive an insight such as “Customer service is less important for malesage 18-25 purchasing Soda Brand 1.” In another implementation, aninsight template may be employed to identify related primals, e.g.,according to client interest.

Generated insights may then be pre-filtered according to a variety ofcriteria 715. For example, in one implementation, a blacklist filter maybe applied to remove and/or modify statements determined to beunsuitable for presentation, such as based on confidence criteria and/orother criteria and/or rules. In another implementation, one or morecolinearity filters may be applied, such as in order to determine if aninsight identifies a specific property of a primal category of interest(e.g., males aged 18-25) or of a broader category encompassing thesub-category (e.g., all males), possibly discarding or demoting theinsight in the latter case. In another implementation, one or moreconfidence filters may be applied, such as to sort, discard, and/ordemote insights based on the volume of underlying corpus documentsinvolved, based on the significance (e.g., statistical significance) ofthe relation between primals in the document corpus, and/or the like.

Filtered insights may then be ranked, such as by assigning one or moreranking measures 720 and ranking and/or tiering the insights accordingto the evaluated values of the measures for those insights 725. Tieringmay be accomplished, for example, by determining an insight rating scorebased on the ranking measures and comparing that score to one or moretier thresholds. In some embodiments, rankings and/or ratings may bemade based on a variety of factors, such as but not limited to: degreeand/or direction of change of a primal value and/or relationship; changeof a primal value with respect to a reference; insight diversity;association of primals to client interest (e.g., according to arules-based system); insight actionability (e.g., the likelihood andamount that a primal and/or primal trend associated with the insight canbe changed, such as based on a calculation of primal elasticity, arules-based system, and/or the like); likely return-on-investment(“ROI”) (e.g., according to an evaluation of actionability andassociated action costs); and/or the like. In one implementation,insight records may be associated with one or more action records, theaction records defining an action to take in response to the insight.Action records may be employed, for example, to generate an action-itemreport of recommended actions in association with a given set ofinsights. In another implementation, action records may be configured toautomatically implement one or more actions in response to the detectionof an insight (e.g., automated ad purchase and/or placement in responseto the identification of a particular insight trend).

A determination may then be made as to whether the ranked and/or tieredlisting of insights should be further curated 730. If so, curationinputs are received 735 and applied 740 to the insight ranks and/ortiering. For example, in one implementation, a system administrator maybe allowed to manually adjust insight rankings and/or tiers. Insighttiers and/or rankings are adjusted and assigned 745 and provided fordisplay 750, such as at a client terminal via an electroniccommunication network.

In one embodiment, an IDAP module (e.g., the Filtering/Ranking Component1147) may generate a set of insights in every time period (e.g., hourly,daily, weekly, monthly, according to a user-customized period, and/orthe like), on a triggered basis, on demand, and/or the like. In oneexample, the top insights for a given time period may be exposed to thecustomer, such as via a client interface. The user may then performfurther operations on each insight to drill down to additional facts orinformation associated therewith, access related insights, and/or thelike.

In one embodiment, a process for generation of insights, top insights,and/or the like may include insight generation, pre-filtering,assignment of measures, tiering, curation and/or publishing of results,and/or the like. In one implementation, a generation layer may generateall possible combinations of insights over all types. In oneimplementation, a pre-filtering layer may apply one or more filters,such as a “blacklist filter” to prune disallowed statements,low-confidence statements, and/or other undesirable results. In oneimplementation, a measure assigning layer may, for each insight, assignmeasures such as, but not limited to, a relevance points (e.g., aBoolean score identifying whether or not an insight qualifies asrelevant to a particular user input), a strength score (e.g.,classifying the relative strength of the insight in relation to aparticular user input, other related insights, and/or the like), and/orthe like.

In one implementation, a tiering layer may apply rules and/or logic overmeasures to break insights into tiers. For example, in oneimplementation, a top tier may include insights as instructed by rulesand/or logic for presentation to the client display. In oneimplementation, a second tier may be identified and/or prepared forpresentation (e.g., to client services, reporting module, backenddiagnostics and/or monitoring services, and/or the like). In oneimplementation, within each tier, insights may be sorted according to astrength score and/or classification. In one implementation, thestrength score and/or classification may be reflected in client-sidedisplay presentation, such as in the form of a numerical score, Booleanscore, discrete labeling, color-coding, highlighting, and/or the like.In various implementations, some or all of the tiering, sorting, and/orscoring of insights may be included in one or more reports and/or may bepersisted in data records associated with the insights, user account,query, and/or the like.

In one implementation, a curation and/or publishing layer may exposeinsights (e.g., with one or both of measures and/or tiering informationincluded) to a client-side display, to a client services display,reporting module, backend monitoring and/or diagnostics, and/or thelike. In one implementation, insights presented to a client servicesdisplay may be manipulated, such as to allow manual assignment of one ormore insights to one tier or another, adjustment of measures, strengthscores, strength classifications, and/or the like.

In one implementation, a pre-filtering layer may apply rules and/orlogic by which insights are filtered out. Pre-filtering may, in someimplementations, filter out insights as they are generated by thegeneration layer and/or may apply filters at a later stage ofprocessing. In one implementation, the pre-filtering layer may applyconfidence interval filters, such as non-time statements and/or timestatements. Non-time statements may, for example, include a confidenceinterval overlap check, whereby an insight is discarded if there is anoverlap between the confidence interval for it and a contrary insight(e.g., if a topic intent fraction for soda brand 1 for aspartameoverlaps one for soda brand 2, the insight “Aspartame is important forconsumers driving soda brand 1 churn relative to soda brand 2” isdiscarded). In another example, non-time statements may include asignificance check, whereby an insight is discarded if an occurrencecount if less than a threshold (e.g., the insight “Bottle is strongerfor customers driving beer brand 1 sales compared to beer brand 2” isdiscarded unless topic “Bottle” has a count above a threshold limit). Inone implementation, time statements may, for example, include azero-interval test, whereby a check is made as to whether a topic intentfraction actually increases or decreases over time and/or in conjunctionwith changes in a secondary variable. In one implementation, an insightmay be discarded unless a zero-interval test indicates an increasingintent fraction across at least two points in time. In oneimplementation, time statements may, for example, include a confidenceinterval check for changes in topic intent fraction, e.g., for a givenbrand and a given competitor brand, to check for overlap. In oneimplementation, such a time statement confidence interval overlap checkmay be implemented in a manner similar to the non-time statementconfidence interval overlap check described above.

In one implementation, pre-filtering may include filtering based on achange in counts. For example, the counts (e.g., of occurrences in acorpus of documents) governing a particular statement, insight, and/orthe like, may be compared to a threshold limit to determine whether ornot to filter them out, e.g., from client presentation.

A variety of measures may be assigned in a measure assignment layer,such as but not limited to relevance points, strength score and/orclassification, and/or the like. In one implementation, relevance pointsand/or a relevance score may take integer values. Relevance points maybe assigned, for example, to account for and/or add up points of one ormore conditions that are to be satisfied in order to identify and/orlabel desirable and/or displayable insights. In one implementation, suchconditions and/or the associated assignment of relevance points may beestablished based on evaluation of the relevance of prior insightresults, user-feedback of such relevance, client services feedback,manual adjustment of relevance ratings, and/or the like. In oneimplementation, relevance points may be collapsed into a single and/orsmall number of metrics (e.g., a sum of relevance points).

In one implementation, heuristic points may be assigned to one or moreinsights. Heuristic points may, in one implementation, be associatedwith relevance and/or be used in the determination of relevance points.For example, in one implementation, a statement, insight, and/or thelike may receive a point for each condition that applies to it, such asany of the following: time based statements (t); topic based statements(TC/TDC, TD and their t variants); recentness of statement (e.g.,measured by the age of the statement, when the statement was first true,and/or the like); target topic; competitive statements (C primalstatements); target demographic; and/or the like. In someimplementations, point assignment for conditions may be weighted, withsome conditions contributing more points than others, such as may bedetermined and/or enforced by a condition weighting schedule.

In one implementation, a strength score may be assigned to insights. Forexample, in one implementation, the strength score may be a positivecontinuous number. The strength score may, for example, signify animportance for a given statement based on specified criteria, such asfinancial impact, impact on other metrics, confidence, and/or the like.In one implementation, a strength score may be determined based on a“delta principle,” identifying how much one or more related values orfactors would have to change in order to negate the statement made inthe given insight. For example, a determination may be made as to howmuch a topic, demographic, intent fraction, and/or the like would haveto change in order to negate the statement. In one implementation, aconservative distance between confidence intervals of the fractions maybe taken, again carrying on the same delta principle. Under thisprinciple, in one implementation, the minimum may be taken at 0 and themaximum at 1 to identify the strength of a given statement.

In one implementation, a strength classification may be assigned (e.g.,as a label, discrete score, Boolean, and/or the like) separately, inaddition to, and/or based on the strength score. In one implementation,strength classification may take values such as, but not limited to, lowstrength, medium strength and high strength. In one implementation,assignment of statements to a strength classification may be made basedon the comparison of a strength score to one or more threshold values.For example, in the delta principle example described above, a strengthscore in the range of 0-0.02 may be labeled as low strength, 0.02-0.08as medium strength, and 0.08-1.00 as high strength. Different rangesand/or thresholds may be used based on the needs and/or desires of aparticular implementation.

In one implementation, a tiering layer may assign the statements,insights, and/or the like to tiers based on factors such as relevancepoints, strength score and/or classification, diversity, confidence,and/or the like. In one implementation, an aggregate score may bedetermined based on one or more such factors, and tiering assignmentsmade on the basis of that aggregate score, such as by comparing it toone or more tiering threshold values. In one implementation, insightsintended for client presentation are assigned to a top tier. In oneimplementation, insights assigned to a second tier may be provided assuggestions and/or may be viewable by client services and may bereassigned, e.g., manually, to the top tier for client presentation. Inone implementation, second tier insights may be available for clientpresentation such as based on user request, indication of insufficienttop tier insights, and/or the like. In some implementations, insightsmay be further subdivided and/or assigned to further tiers based oninsight handling and/or presentation purposes (e.g., some insights maybe assigned to a tier 0 for presentation at the top of every insightpresentation). In one implementation, within each tier the insights maybe sorted, such as based on strength score, classification, and/or thelike. Insight presentation may further include presentation of sortingcriteria, strength scores, strength classifications, and/or the like. Insome implementations, measures of relevance, strength, and/or the likemay look at each insight in isolation, while tiering allows forcross-insight logic (e.g., diversity). In one implementation, insightsmay be sorted by relevance points to identify a plurality of relevancelevels, and then sorted by a strength classification, until a top N(e.g., 5) insights can be identified.

In one implementation, diversity may be accounted for, such as limitingthe number of insights of a given type to a certain threshold limit.Insights of a given type matching a top insight may be bumped down(e.g., in terms of relevance points, strength score, strengthclassification, and/or the like) until, e.g., a top N number of insightsof other types can be identified. In one implementation, insights of agiven type that are demoted based on a diversity condition may beflagged (e.g., “Demoted due to diversity) in association with an insightand/or insight scoring record, such as for client services presentation.

In one implementation, a curation and/or publication layer may determinewhat is displayed at a client system, client services system, reportingmodule, and/or the like. In one implementation, curation may act as afail-safe mode, and may include one or more of the following operations:curator accepts top tier completely; curator manually and/orautomatically rearranges insights across tiers (e.g., withnon-generalizable reasons); curator manually and/or automaticallyrearranges insights across tiers (e.g., with a reason that isgeneralizable and could be fed back into the rules-based filteringand/or sorting described above).

In one implementation, the only metrics displayed to the client systemare the insights, their ranks, and their classifications. In oneimplementation, drill-ins, tracking, and/or the like may be provided forpresentation, such as upon request. For example, a drill-in may includeinformation about one or more sources of an insight, confidence levels,additional metrics, related information, and/or the like. In oneimplementation, a chapter may be characterized as a tier (e.g.,collection of insights) over a given period of time, and may bedisplayed with time resolution, tracked over time, and/or the like.

Aspects of an embodiment of an IDAP user interface are shown in FIGS.12A-12I.

Topic Builder

In various embodiments, the IDAP may be configurable as a topic builder,permitting users to discovery topics, tags, labels, and/or the like toassign to documents in a corpus and/or to facilitate highly optimizedqueries over volumes of data. For example, the IDAP may act as a tooland/or process to allow one or more users to build optimized queriesquantified, for example, by query precision and recall, such as for usein connection with future and/or updated collections of structuredand/or unstructured data.

In some implementations, the IDAP may provide a supervised feedback loopwhich may be engaged by users to build ontologies (e.g., clusters ofthings, words, people, images, web pages, traits, behaviors, and/or thelike) via interactions with structured and/or unstructured data.Optimized queries, topics, tags, labels, metadata, and/or the likeconstructed via topic builder embodiments of the IDAP may be employed ina variety of contexts, such as for sharing in multi-user environments,for using on an evolving document corpus, and/or the like. For example,in one embodiment, a document corpus may be drawn from a social mediadata feed, such as Twitter posts and/or the like, and updated inrea-time, near real-time, periodically, on a triggered basis, and/or thelike.

FIG. 8 shows aspects of exploration mode logic flow for a topic builderin one embodiment. A corpus of documents is collected 805, which maycomprise a collection of structured and/or unstructured data such associal media feeds (e.g., text, audio, images, video, and/or the likedocuments, media, unstructured data and/or the like; and includingsources such as Twitter, Facebook, Google+, Instagram, Snapchat, and/orthe like). The document corpus may be updated, changed, supplemented,and/or the like over the course of a topic building process and/or aftertopic building, so as to apply optimized topics, queries, tags, labels,and/or the like to new data. A determination may be made as to whetherone or more filters are to be applied to documents in the corpus 810.For example, documents may be filtered according to type, format, size,content, metadata, source, relevance criteria, and/or the like. Iffiltering is desired, one or more filters may be applied to sampleand/or narrow the documents in the corpus 815. A determination may alsobe made as to whether weighting is to be applied to documents in thecorpus 820, such that some documents factor more prominently than othersin the determination of topics to follow. Any desired weighting may beapplied 825. Topics are then acquired from the document corpus 830,where each topic comprises a query term in connection with relatedterms, identified via relationships (e.g., proximity, natural languagerelationship, parts-of-speech analysis, syntax analysis and/or othergrammatical analysis, and/or the like) gleaned from documents in thecorpus. Topics may be stored, such as in association with the corpus,specific documents, sources, anticipated uses, and/or the like. Adetermination may be made as to whether the document corpus should berebalanced, updated, or otherwise modified 835 and, if so, then the flowmay return to 805, 810 and/or 820 to collect new documents, applyfilters, and/or weights. Otherwise, the flow may conclude 840 and/orproceed to evaluation mode, such as shown in one embodiment in FIG. 9

FIG. 9 shows aspects of evaluation mode logic flow for a topic builderin one embodiment. In one embodiment, a topic builder evaluation modemay be employed to build topic sets where, in one implementation, eachtopic set may comprise a topic together with a collection of queriesmade from the topic and associated precision and recall valuesassociated with each query. In one implementation, precision and/orpositive prediction value may be quantified as a fraction of retrievedresults relevant to a user's query. In one implementation, recall and/orsensitivity may be quantified as a fraction of relevant instances thatare successfully retrieved in response to a query. Precision and recall(or “P & R”) may be used to quantify the quality of queries, such as forquery optimization. A user may submit a query in association with atopic over a document corpus and/or a subset thereof 905. A query may,for example, comprise a Boolean query over one or more terms, such as aquery term and related terms associated with the query term as part of atopic. A query may, in some implementations, comprise a natural languagequery; a media-based query (e.g., image, audio, video, or other signal);and/or the like. A determination may be made regarding which documentsto present 910, such as all matching query results and/or a sampleand/or subset thereof, other results from the corpus and/or a subsetand/or sample thereof, and/or the like. If a subset is desired, then thesample is acquired 915, such as may be based, for example, on documenttype, content, metadata, source, and/or the like. Query results may bepresented for display to the user 920. In some implementations,displayed results may include results identified based on the submittedquery and/or other results from the corpus not necessarily found as aresult of the query. Inclusion of the latter may, for example,facilitate the determination of a recall metric, to identify aproportion of query-matching results in the document corpus that are notreturned as a result of the query. The user may then rate presentedresults 925, such as according to precision and/or recall metrics. Inone implementation, documents and/or other query results may be ratedaccording to whether they match the query and/or desired results; don'tmatch the query and/or desired results; can't determine whether theymatch the query and/or desired results; and/or the like. In someimplementations, users may be permitted to enter a confidence intervaland/or other fuzzy logic metrics, e.g., in order to quantify a degree ofcertainty regarding whether results match the query and/or desiredresults. In some implementations, weights may be applied to ratings,such as according to rating confidence intervals; corpus and/or queryresult volume; rater authority, experience, title, role, rank; and/orthe like. A determination may be made as to whether a sufficient numberof documents have been rated 930. For example, in one implementation, afixed threshold number of documents must be rated before the flowproceeds to determining an overall precision and/or recall. In anotherimplementation, a dynamic threshold number of documents may bedetermined and enforced, such as may be based on the number of matchingquery results and/or the sparseness of a query term across the documentcorpus. For example, in one implementation, a minimum number of ratedresults and/or documents may be determined according to a formula suchas the following: Min=C/(# matching documents), where C is a constant(such as C=400). If at least a minimum number of documents has beenrated, then a precision and/or recall may be determined for the query935. For example, a precision may be determined as a ratio of the numberof documents rated as matches to the total number of documents retrievedby the query. In another implementation, a precision may be determinedas a ratio of a weighted number of matches, each weighted according to,e.g., a confidence metric, to the total number of retrieved results. Inone implementation, a recall may be determined as a ratio of resultsretrieved in response to the query that are marked as matches to thetotal number of documents marked as matches. In another implementation,a recall may be determined as a ratio of a ratio of a weighted number ofmatches, each weighted according to, e.g., a confidence metric, to thetotal number of documents marked as matches. A topic set may be storedas an association between a topic, queries built from the topic, andprecision and/or recall metric values determined for each query 938. Adetermination may be made as to whether to feedback ratings into asubsequent sampling of documents from the corpus in response to a querysubmission 940. For example, documents rated as non-matches in responseto a particular query may be excluded from query results for asubsequent query submission. In another example, documents not retrievedin response to a particular query that are nevertheless marked asmatching the query may be included as results in response to subsequentsubmissions of that query. If desired, ratings are fed back intosubsequent sampling of documents 950 and/or may influence subsequentuses of the corresponding query terms. If no feedback is desired, thenthe flow may conclude 955.

Influence Discovery

In some embodiments, the IDAP may be configurable for influencediscovery across social media and/or other structured and/orunstructured document sources. For example, the IDAP may be configuredto identify subsets of social media users responsible for driving one ormore global metrics (e.g., sales, subscriptions, and/or the like) fromunstructured data, e.g., without requiring exemplars, templates, and/orthe like for such users in advance. In one embodiment, users may beidentified by identifying associated user-generated data whose behavioris correlated with one or more global metrics of interest.

FIG. 10 shows aspects of logic flow for social media influence discoveryin one embodiment. A term may be pulled from a list of terms 1005, andmay in various implementations, comprise a word, phrase, image, sound,action, behavior, and/or the like. A count of instances of the number ofuses and/or users employing that term may be determined per unit time(e.g., per day, week, month, quarter, year, and/or the like) 1010, toyield a count time-series for that term. In one implementation, wherethe number of observations of term usage are limited in a given period,a logistic regression may be performed to fill in missing informationand/or estimate a count value for a given period. A determination may bemade as to whether count time-series are to be determined for otherterms 1015 and, if so, the flow may return to 1005 to choose the nextterm in the list. In one implementation, a correlation of counttime-series for different terms may be determined 1020, and terms havinga sufficient degree of correlation may be grouped 1025, such as in orderto reduce the total term count. In one implementation, the correlationof count time-series for different terms may be determined as a Pearsoncorrelation. In another implementation, the correlation of counttime-series for different terms may be determined as a Pearsoncorrelation with Sobolev Extension.

A global metric time-series may then be accessed and compared with oneor more term and/or term group count time-series to determinecorrelations 1030. In one implementation, the correlation of counttime-series with global metric time-series may be determined as aPearson correlation. In another implementation, the correlation of counttime-series with global metric time-series may be determined as aPearson correlation with Sobolev Extension. A wide variety of differentglobal metrics may be considered based on the needs and/or desires of aparticular user and/or use scenario. For example, global metrics mayinclude sales, revenues, profits, subscriptions, service usages, votes,social media activities, and/or the like. Thus, for example, a globalmetric time-series could comprise revenue per quarter, sales per month,Facebook updates per day, votes per election cycle period, and/or thelike. Correlations determined at 1030 may be compared to a threshold toidentify terms whose count time-series are highly correlated with theglobal metric time-series 1035. In one implementation, a correlationthreshold may be a fixed amount. In another implementation, acorrelation threshold may be determined dynamically, such as based on acorrelation determined between a global metric time-series and a termcount time-series drawn from random samples of the document corpus. Inone implementation, where the correlation does not exceed the thresholdat 1035, the correlation calculation at 1030 may be repeated withre-sampled time-series 1040, such as by dropping random dates and/orperiods in order to eliminate the impact of a small group of spuriousdeviations. Most highly correlated terms (e.g., top 10-15 of them froman original list of 5,000-10,000) may be identified, e.g., as intentdrivers, and/or stored 1045. In some implementations, users employinghighly correlated terms historically and/or on a forward-looking basismay be identified (e.g., as “influencers” or “super users”) and/ormonitored for term usage and/or other activity which may impact globalmetric values. For example, usage of identified terms by influencers maybe detected in order to predict expected behaviors of global metric, asdetermined previously from historical correlations. In anotherimplementation, individual user behaviors and/or term usages may bepredicted from monitoring and/or detection of global metric behaviorsand/or patterns.

In some implementations, a multi-metric analysis may be employed,wherein time-series for two or more global metrics are evaluated in amulti-dimensional correlation with term count time-series. In someimplementations, users may be identified as “influencers” or “superusers” based on other criteria (e.g., experience, rank, role, title,activity levels, number of social media accounts, number of social mediaconnections, and/or the like) in conjunction with usage of terms highlycorrelated with global metrics. In some implementations, time-seriescorrelations may be calculated as weighted sums and/or integrals overtwo time variables. For example, in one implementation, a correlationmay be determined according to a formula similar to the followingexample:correlation=∫∫δ(t ₁ −t ₂)x(t ₁)y(t ₂)dt ₁ dt ₂

Where, in the example above, x and y are count time-series and/or globalmetric time-series over time variables t₁ and t₂, and δ is a weightingfactor which takes into account correlations of events occurring atdifferent times. In some implementations, specific frequency componentsof count and/or global metric time-series may be isolated, e.g., viaFourier analysis, in order to determine correlation of those frequencycomponents in isolation. Thus, for example, correlations of fast orslowly varying term usage and/or global metric behavior may bedetermined separately. In some implementations, series over one or moreindependent variables other than and/or in addition to time may beemployed (e.g., geography) to identify correlations between term usageand one or more global metrics.

FIGS. 11A-11D show a table of primals and related insights in oneembodiment. A primals column 1101 lists various combinations of primals,such as topic (T), time (t), demographic (D), competitor brand (C),and/or the like. Thus, for example, a “T t” combination may signifyinsights related to a particular topic tracked over a period of time,while “T C” combination may signify insights related to a particulartopic in comparison to performance of a competitor brand. A wide varietyof other primals and/or combinations thereof may be employed in otherimplementations of IDAP operation. A “Statement in English” column 1105lists insight descriptions and/or examples of how an insight may bepresented, e.g., to a client system, client services system, reportingmodule, backend database, and/or the like. For example. for a “T t”primal combination, the statement in English may reflect that the topicT increased in importance for driving customers to a target brand and/orin driving one or more other key performance indicators (KPIs). An“Examples” column 1110 provides some examples of the insight in specificinstances. A “Time Based Footnote” 1115 indicates one or more timesand/or time periods over which a particular insight may be determined.For example, in an insight having primal t may be computed at a currenttime and/or time interval in comparison to a prior time and/or timeinterval, while an insight not having primal t may be computed over asingle baseline period. A “Computation” column 1120 may evaluate intent(I), such as consumer intent, in relation to primals associated with agiven insight in accordance with formulas and/or conditions similar tothe displayed examples. A “Display Metrics” column 1125 includes one ormore additional metrics related to the insight and/or its associatedprimals which may also be displayed in addition to and/or instead of theinsight statement. A “Qualifiers” column 1130 includes examples ofqualifiers which may be included in an insight in association with oneor more primals, such as “increasing,” “decreasing,” and “neutral” todescribe time-based variations in an insight parameter. A “VisualRepresentation” column 1135 includes a listing of displaycharacteristics, user interface features, and/or the like for display bya client system, client services system, reporting module, and/or thelike in association with an insight and/or insight primals. Finally, a“Comments” column 1140 includes additional remarks about each of theindicated insights and/or associated primals for the illustratedembodiment.

FIGS. 12A-12I show aspects of user interface in embodiments of IDAPoperation. FIG. 12A shows an example of an insight directory screen,displaying selectable insight categories 1201 for a given brand “U.S.Motors” 1205. In one implementation, the displayed insight categoriesmay be previously set up by the user, client system, IDAP administrator,client services system, and/or the like, and may be subsequentlyretrievable, such as to view updates or other changes. In anotherimplementation, a client system may automatically generate and displayinsight categories, such as by selecting top insights (e.g., accordingto relevance points, strength score and/or classification, and/or thelike) for display and grouping them into categories in an insightdirectory interface. FIG. 12B shows an example of an insight screen,where a user has selected a particular insight from options such asthose shown in FIG. 12A. In this case, the user has selected the “LuxuryCrossover Sales” insight, and the interface may include one or moreelements 1210 to allow selection of one or more other insights directlywithout returning to a prior screen. The screen also may include aselectable date range for the insight 1215, allowing the user to select,for example, a current date, a tracked prior date, date range, and/orthe like. The screen may also list one or more insights 1220 related tothe selected insight category. In one implementation, displayed insights1220 may be selectable, allowing a user to drill down into furtherinformation related to the insight, such as by clicking on the insight.FIG. 12C shows an example of a drill-down screen for a selected insight,in this case the insight, “Family Safety increased in importance forcustomers driving New Car Sales over last month” 1220, based on thetopic “Family Safety” in this instance. The screen may include a furtherdescription of insight parameters 1225, such as the topic. The screenmay further include further information about the insight, such as acollection of importance ratings for the specific topic in the insight(family safety) 1230 and/or for related topics (e.g., fuel efficiency,dealership experience, financing options, and/or the like) 1235. In thedisplayed implementation, the screen may show the relative importance ofeach topic to the insight (e.g., how much each topic drives new salesover a given time period) for the target brand in comparison to one ormore competitor brands, across different products, in different regions,and/or the like. In one implementation, the relative importance may alsobe displayed with an error bar reflecting statistical features (e.g.,confidence, variance, standard deviation, and/or the like) of aparticular importance value. The screen may further provide ademographic breakdown of the importance of one or more topics for thetarget brand and/or one or more competitor brands 1240. FIG. 12D showsan example of a screen showing further insight drill-down information,such as an alternate display of a demographic breakdown of topicimportance 1245 and a comparison of topic importance across a pluralityof competitors 1250. Such a display may, for example, allow a user toidentify which demographic groups and/or competitor brands are mostimpacted, in terms of product sales, by a given topic (e.g., sales ofCompetitor 1's product are mostly driven by fuel efficiencyconsiderations; females under 40 are most concerned about family safetyin purchasing the target brand's product). FIG. 12E shows an example ofa screen showing graphical breakdown of insight information, includingimportance of an insight topic over time for a target brand andcompetitor brand 1255. In one implementation, the graphicalrepresentation may be selectable 1260, allowing a user to identify aspecific time at which the topic importance is to be evaluated and/ordisplayed. FIG. 12F shows an example of a screen that may appear inresponse to a selection of a specific time at 1260 in FIG. 12E, wherethe topic importance for the target brand and competitor at the selectedtime are displayed 1260. FIG. 12G shows an example of a screen forinsight sharing. The screen may include a variety of interface elementsfacilitating the sharing of insights, such as with other IDAP users,non-users, and/or the like. A file format element 1265 may allow a userto define reporting parameters and/or display characteristics, such asthe size, orientation, aspect ratio, and/or the like. A type element1267 may allow a user to further define a filetype (e.g., PDF, JPG,and/or the like) for the shared insight. Additional elements 1269 may beprovided to allow for further customization of the shared insight, suchas including a cover page, glossary (e.g., providing information similarto that shown at 1225 in FIG. 12C), report configuration parameters,and/or the like. The screen may also include one or more selectableoptions for how the insight should be shared 1271, such as an option todownload the insight, email the insight, and/or the like. FIG. 12H showsan example of a screen for sharing an insight by email, withauto-population of an email with the insight 1272. In someimplementations, the IDAP may include email attachments as well, such asthe insight, related insights, glossary, cover page, reportingconfiguration, and/or the like. FIG. 12I shows an example of a screensimilar to the drill-down screen of FIG. 12C, except including a furtherinterface element 1274 facilitating changing of a time for insightevaluation.

In some implementations, the IDAP may be configurable to discoversignals from among a wide variety of media sources, such as but notlimited to social media, social comments, third party financial data,and/or the like, that drive sales or other aspects of business and/ormarketing strategy. Intentful conversations extracted from such sourcesmay be identified and correlated to one or more KPIs. In oneimplementation, analysis of large volumes (e.g., multi-terabyte)annotated data sets containing billions of social comments and millionsof users over thousands of different dimensions may be performed tobuild models and compute counts. The IDAP may, for example, employ textsearching, natural language processing tools, and/or the like, as wellas flexible time series analysis, random sampling, top K analysis,and/or the like.

Media Efficacy

In some embodiments, the IDAP may be configurable to evaluate efficacyand/or return on investment of advertising and/or other media campaignsand/or to recommend actions for improvement thereof. For example, in oneimplementation the IDAP may employ influence discovery tools such asthose described above and in relation to FIG. 6 in order to identifyactions, activities, terms, phrases, images, company behaviors, spendingpatterns, and/or the like that are highly correlated with global metricbehaviors and/or patterns. In some implementations, multi-facetedcampaigns of media and/or advertising behavior (e.g., including one ormore of: internet advertising, television advertising, radioadvertising, print advertising, social media publication, productplacement, and/or the like) may be considered as a whole in relation toglobal metric behaviors and/or patterns in order to evaluate theefficacy and/or return on investment associated with the campaign as awhole.

In one implementation, a particular test corpus may be compared againsta broader control corpus for identification of trends with respect toone or more global metrics. For example, an advertiser may focus on atest group comprising watchers of a particular television program andcompare this with a control group comprising watchers of television ingeneral. Correlations of test group activities, documents, data, and/orthe like with the global metric may then be compared with correlationsfor the same information of the control group in order to determine therelative efficacy of focusing advertisements on the test group. Inanother implementation, a test group and control group may comprise thesame group at different times.

In another implementation, return on investment may be determined forsponsorship campaigns, e.g., involving sports, teams, celebrities,and/or the like. For example, celebrity followers in social data may bemonitored to identify changes in the usage of global metric correlatedintent drivers before and after a campaign. In one implementation, acontrol group may be comprised of general users, not necessarilycelebrity followers, who are also using the same intent drivers, inorder to determine the relative efficacy of the sponsorship campaignwith respect to the particular celebrity.

In one implementation, various factors which may influence the volume ofintent drivers in social data may be accounted for in order to notunduly influence results. For example, account may be taken of factorssuch as, but not limited to: scaling of the number of social media usersover time, geography, from one social media service to another, and/orthe like; census and/or other demographic variations over time,geography, and/or the like; seasonal variations in social media usage(e.g., associated with the release of a new mobile device, weather,and/or the like).

IDAP Controller

FIG. 13 shows a block diagram illustrating embodiments of a IDAPcontroller. In this embodiment, the IDAP controller 1301 may serve toaggregate, process, store, search, serve, identify, instruct, generate,match, and/or facilitate interactions with a computer through marketanalysis technologies, and/or other related data.

Typically, users, which may be people and/or other systems, may engageinformation technology systems (e.g., computers) to facilitateinformation processing. In turn, computers employ processors to processinformation; such processors 1303 may be referred to as centralprocessing units (CPU). One form of processor is referred to as amicroprocessor. CPUs use communicative circuits to pass binary encodedsignals acting as instructions to enable various operations. Theseinstructions may be operational and/or data instructions containingand/or referencing other instructions and data in various processoraccessible and operable areas of memory 1329 (e.g., registers, cachememory, random access memory, etc.). Such communicative instructions maybe stored and/or transmitted in batches (e.g., batches of instructions)as programs and/or data components to facilitate desired operations.These stored instruction codes, e.g., programs, may engage the CPUcircuit components and other motherboard and/or system components toperform desired operations. One type of program is a computer operatingsystem, which, may be executed by CPU on a computer; the operatingsystem enables and facilitates users to access and operate computerinformation technology and resources. Some resources that may beemployed in information technology systems include: input and outputmechanisms through which data may pass into and out of a computer;memory storage into which data may be saved; and processors by whichinformation may be processed. These information technology systems maybe used to collect data for later retrieval, analysis, and manipulation,which may be facilitated through a database program. These informationtechnology systems provide interfaces that allow users to access andoperate various system components.

In one embodiment, the IDAP controller 1301 may be connected to and/orcommunicate with entities such as, but not limited to: one or more usersfrom user input devices 1311; peripheral devices 1312; an optionalcryptographic processor device 1328; and/or a communications network1313.

Networks are commonly thought to comprise the interconnection andinteroperation of clients, servers, and intermediary nodes in a graphtopology. It should be noted that the term “server” as used throughoutthis application refers generally to a computer, other device, program,or combination thereof that processes and responds to the requests ofremote users across a communications network. Servers serve theirinformation to requesting “clients.” The term “client” as used hereinrefers generally to a computer, program, other device, user and/orcombination thereof that is capable of processing and making requestsand obtaining and processing any responses from servers across acommunications network. A computer, other device, program, orcombination thereof that facilitates, processes information andrequests, and/or furthers the passage of information from a source userto a destination user is commonly referred to as a “node.” Networks aregenerally thought to facilitate the transfer of information from sourcepoints to destinations. A node specifically tasked with furthering thepassage of information from a source to a destination is commonly calleda “router.” There are many forms of networks such as Local Area Networks(LANs), Pico networks, Wide Area Networks (WANs), Wireless Networks(WLANs), etc. For example, the Internet is generally accepted as beingan interconnection of a multitude of networks whereby remote clients andservers may access and interoperate with one another.

The IDAP controller 1301 may be based on computer systems that maycomprise, but are not limited to, components such as: a computersystemization 1302 connected to memory 1329.

Computer Systemization

A computer systemization 1302 may comprise a clock 1330, centralprocessing unit (“CPU(s)” and/or “processor(s)” (these terms are usedinterchangeable throughout the disclosure unless noted to the contrary))1303, a memory 1329 (e.g., a read only memory (ROM) 1306, a randomaccess memory (RAM) 1305, etc.), and/or an interface bus 1307, and mostfrequently, although not necessarily, are all interconnected and/orcommunicating through a system bus 1304 on one or more (mother)board(s)1302 having conductive and/or otherwise transportive circuit pathwaysthrough which instructions (e.g., binary encoded signals) may travel toeffectuate communications, operations, storage, etc. The computersystemization may be connected to a power source 1386; e.g., optionallythe power source may be internal. Optionally, a cryptographic processor1326 and/or transceivers (e.g., ICs) 1374 may be connected to the systembus. In another embodiment, the cryptographic processor and/ortransceivers may be connected as either internal and/or externalperipheral devices 1312 via the interface bus I/O. In turn, thetransceivers may be connected to antenna(s) 1375, thereby effectuatingwireless transmission and reception of various communication and/orsensor protocols; for example the antenna(s) may connect to: a TexasInstruments WiLink WL1283 transceiver chip (e.g., providing 802.11n,Bluetooth 3.0, FM, global positioning system (GPS) (thereby allowingIDAP controller to determine its location)); Broadcom BCM4329FKUBGtransceiver chip (e.g., providing 802.11n, Bluetooth 2.1+EDR, FM, etc.);a Broadcom BCM4750IUB8 receiver chip (e.g., GPS); an InfineonTechnologies X-Gold 618-PMB9800 (e.g., providing 2G/3G HSDPA/HSUPAcommunications); and/or the like. The system clock typically has acrystal oscillator and generates a base signal through the computersystemization's circuit pathways. The clock is typically coupled to thesystem bus and various clock multipliers that will increase or decreasethe base operating frequency for other components interconnected in thecomputer systemization. The clock and various components in a computersystemization drive signals embodying information throughout the system.Such transmission and reception of instructions embodying informationthroughout a computer systemization may be commonly referred to ascommunications. These communicative instructions may further betransmitted, received, and the cause of return and/or replycommunications beyond the instant computer systemization to:communications networks, input devices, other computer systemizations,peripheral devices, and/or the like. It should be understood that inalternative embodiments, any of the above components may be connecteddirectly to one another, connected to the CPU, and/or organized innumerous variations employed as exemplified by various computer systems.

The CPU comprises at least one high-speed data processor adequate toexecute program components for executing user and/or system-generatedrequests. Often, the processors themselves will incorporate variousspecialized processing units, such as, but not limited to: integratedsystem (bus) controllers, memory management control units, floatingpoint units, and even specialized processing sub-units like graphicsprocessing units, digital signal processing units, and/or the like.Additionally, processors may include internal fast access addressablememory, and be capable of mapping and addressing memory 1329 beyond theprocessor itself; internal memory may include, but is not limited to:fast registers, various levels of cache memory (e.g., level 1, 2, 3,etc.), RAM, etc. The processor may access this memory through the use ofa memory address space that is accessible via instruction address, whichthe processor can construct and decode allowing it to access a circuitpath to a specific memory address space having a memory state. The CPUmay be a microprocessor such as: AMD's Athlon, Duron and/or Opteron;ARM's application, embedded and secure processors; IBM and/or Motorola'sDragonBall and PowerPC; IBM's and Sony's Cell processor; Intel'sCeleron, Core (2) Duo, Itanium, Pentium, Xeon, and/or XScale; and/or thelike processor(s). The CPU interacts with memory through instructionpassing through conductive and/or transportive conduits (e.g., (printed)electronic and/or optic circuits) to execute stored instructions (i.e.,program code) according to conventional data processing techniques. Suchinstruction passing facilitates communication within the IDAP controllerand beyond through various interfaces. Should processing requirementsdictate a greater amount speed and/or capacity, distributed processors(e.g., Distributed IDAP), mainframe, multi-core, parallel, and/orsuper-computer architectures may similarly be employed. Alternatively,should deployment requirements dictate greater portability, smallerPersonal Digital Assistants (PDAs) may be employed.

Depending on the particular implementation, features of the IDAP may beachieved by implementing a microcontroller such as CAST's R8051XC2microcontroller; Intel's MCS 51 (i.e., 8051 microcontroller); and/or thelike. Also, to implement certain features of the IDAP, some featureimplementations may rely on embedded components, such as:Application-Specific Integrated Circuit (“ASIC”), Digital SignalProcessing (“DSP”), Field Programmable Gate Array (“FPGA”), and/or thelike embedded technology. For example, any of the IDAP componentcollection (distributed or otherwise) and/or features may be implementedvia the microprocessor and/or via embedded components; e.g., via ASIC,coprocessor, DSP, FPGA, and/or the like. Alternately, someimplementations of the IDAP may be implemented with embedded componentsthat are configured and used to achieve a variety of features or signalprocessing.

Depending on the particular implementation, the embedded components mayinclude software solutions, hardware solutions, and/or some combinationof both hardware/software solutions. For example, IDAP featuresdiscussed herein may be achieved through implementing FPGAs, which are asemiconductor devices containing programmable logic components called“logic blocks,” and programmable interconnects, such as the highperformance FPGA Virtex series and/or the low cost Spartan seriesmanufactured by Xilinx. Logic blocks and interconnects can be programmedby the customer or designer, after the FPGA is manufactured, toimplement any of the IDAP features. A hierarchy of programmableinterconnects allow logic blocks to be interconnected as needed by theIDAP system designer/administrator, somewhat like a one-chipprogrammable breadboard. An FPGA's logic blocks can be programmed toperform the operation of basic logic gates such as AND, and XOR, or morecomplex combinational operators such as decoders or mathematicaloperations. In most FPGAs, the logic blocks also include memoryelements, which may be circuit flip-flops or more complete blocks ofmemory. In some circumstances, the IDAP may be developed on regularFPGAs and then migrated into a fixed version that more resembles ASICimplementations. Alternate or coordinating implementations may migrateIDAP controller features to a final ASIC instead of or in addition toFPGAs. Depending on the implementation all of the aforementionedembedded components and microprocessors may be considered the “CPU”and/or “processor” for the IDAP.

Power Source

The power source 1386 may be of any standard form for powering smallelectronic circuit board devices such as the following power cells:alkaline, lithium hydride, lithium ion, lithium polymer, nickel cadmium,solar cells, and/or the like. Other types of AC or DC power sources maybe used as well. In the case of solar cells, in one embodiment, the caseprovides an aperture through which the solar cell may capture photonicenergy. The power cell 1386 is connected to at least one of theinterconnected subsequent components of the IDAP thereby providing anelectric current to all subsequent components. In one example, the powersource 1386 is connected to the system bus component 1304. In analternative embodiment, an outside power source 1386 is provided througha connection across the I/O 1308 interface. For example, a USB and/orIEEE 1394 connection carries both data and power across the connectionand is therefore a suitable source of power.

Interface Adapters

Interface bus(ses) 1307 may accept, connect, and/or communicate to anumber of interface adapters, conventionally although not necessarily inthe form of adapter cards, such as but not limited to: input outputinterfaces (I/O) 1308, storage interfaces 1309, network interfaces 1310,and/or the like. Optionally, cryptographic processor interfaces 1327similarly may be connected to the interface bus. The interface busprovides for the communications of interface adapters with one anotheras well as with other components of the computer systemization.Interface adapters are adapted for a compatible interface bus. Interfaceadapters conventionally connect to the interface bus via a slotarchitecture. Conventional slot architectures may be employed, such as,but not limited to: Accelerated Graphics Port (AGP), Card Bus,(Extended) Industry Standard Architecture ((E)ISA), Micro ChannelArchitecture (MCA), NuBus, Peripheral Component Interconnect (Extended)(PCI(X)), PCI Express, Personal Computer Memory Card InternationalAssociation (PCMCIA), and/or the like.

Storage interfaces 1309 may accept, communicate, and/or connect to anumber of storage devices such as, but not limited to: storage devices1314, removable disc devices, and/or the like. Storage interfaces mayemploy connection protocols such as, but not limited to: (Ultra)(Serial) Advanced Technology Attachment (Packet Interface) ((Ultra)(Serial) ATA(PI)), (Enhanced) Integrated Drive Electronics ((E)IDE),Institute of Electrical and Electronics Engineers (IEEE) 1394, fiberchannel, Small Computer Systems Interface (SCSI), Universal Serial Bus(USB), and/or the like.

Network interfaces 1310 may accept, communicate, and/or connect to acommunications network 1313. Through a communications network 1313, theIDAP controller is accessible through remote clients 1333 b (e.g.,computers with web browsers) by users 1333 a. Network interfaces mayemploy connection protocols such as, but not limited to: direct connect,Ethernet (thick, thin, twisted pair 10/100/1000 Base T, and/or thelike), Token Ring, wireless connection such as IEEE 802.11a-x, and/orthe like. Should processing requirements dictate a greater amount speedand/or capacity, distributed network controllers (e.g., DistributedIDAP), architectures may similarly be employed to pool, load balance,and/or otherwise increase the communicative bandwidth required by theIDAP controller. A communications network may be any one and/or thecombination of the following: a direct interconnection; the Internet; aLocal Area Network (LAN); a Metropolitan Area Network (MAN); anOperating Missions as Nodes on the Internet (OMNI); a secured customconnection; a Wide Area Network (WAN); a wireless network (e.g.,employing protocols such as, but not limited to a Wireless ApplicationProtocol (WAP), I-mode, and/or the like); and/or the like. A networkinterface may be regarded as a specialized form of an input outputinterface. Further, multiple network interfaces 1310 may be used toengage with various communications network types 1313. For example,multiple network interfaces may be employed to allow for thecommunication over broadcast, multicast, and/or unicast networks.

Input Output interfaces (I/O) 1308 may accept, communicate, and/orconnect to user input devices 1311, peripheral devices 1312,cryptographic processor devices 1328, and/or the like. I/O may employconnection protocols such as, but not limited to: audio: analog,digital, monaural, RCA, stereo, and/or the like; data: Apple Desktop Bus(ADB), IEEE 1394a-b, serial, universal serial bus (USB); infrared;joystick; keyboard; midi; optical; PC AT; PS/2; parallel; radio; videointerface: Apple Desktop Connector (ADC), BNC, coaxial, component,composite, digital, Digital Visual Interface (DVI), high-definitionmultimedia interface (HDMI), RCA, RF antennae, S-Video, VGA, and/or thelike; wireless transceivers: 802.11a/b/g/n/x; Bluetooth; cellular (e.g.,code division multiple access (CDMA), high speed packet access(HSPA(+)), high-speed downlink packet access (HSDPA), global system formobile communications (GSM), long term evolution (LTE), WiMax, etc.);and/or the like. One typical output device may include a video display,which typically comprises a Cathode Ray Tube (CRT) or Liquid CrystalDisplay (LCD) based monitor with an interface (e.g., DVI circuitry andcable) that accepts signals from a video interface, may be used. Thevideo interface composites information generated by a computersystemization and generates video signals based on the compositedinformation in a video memory frame. Another output device is atelevision set, which accepts signals from a video interface. Typically,the video interface provides the composited video information through avideo connection interface that accepts a video display interface (e.g.,an RCA composite video connector accepting an RCA composite video cable;a DVI connector accepting a DVI display cable, etc.).

User input devices 1311 often are a type of peripheral device 512 (seebelow) and may include: card readers, dongles, finger print readers,gloves, graphics tablets, joysticks, keyboards, microphones, mouse(mice), remote controls, retina readers, touch screens (e.g.,capacitive, resistive, etc.), trackballs, trackpads, sensors (e.g.,accelerometers, ambient light, GPS, gyroscopes, proximity, etc.),styluses, and/or the like.

Peripheral devices 1312 may be connected and/or communicate to I/Oand/or other facilities of the like such as network interfaces, storageinterfaces, directly to the interface bus, system bus, the CPU, and/orthe like. Peripheral devices may be external, internal and/or part ofthe IDAP controller. Peripheral devices may include: antenna, audiodevices (e.g., line-in, line-out, microphone input, speakers, etc.),cameras (e.g., still, video, webcam, etc.), dongles (e.g., for copyprotection, ensuring secure transactions with a digital signature,and/or the like), external processors (for added capabilities; e.g.,crypto devices 528), force-feedback devices (e.g., vibrating motors),network interfaces, printers, scanners, storage devices, transceivers(e.g., cellular, GPS, etc.), video devices (e.g., goggles, monitors,etc.), video sources, visors, and/or the like. Peripheral devices ofteninclude types of input devices (e.g., cameras).

It should be noted that although user input devices and peripheraldevices may be employed, the IDAP controller may be embodied as anembedded, dedicated, and/or monitor-less (i.e., headless) device,wherein access would be provided over a network interface connection.

Cryptographic units such as, but not limited to, microcontrollers,processors 1326, interfaces 1327, and/or devices 1328 may be attached,and/or communicate with the IDAP controller. A MC68HC16 microcontroller,manufactured by Motorola Inc., may be used for and/or withincryptographic units. The MC68HC16 microcontroller utilizes a 16-bitmultiply-and-accumulate instruction in the 16 MHz configuration andrequires less than one second to perform a 512-bit RSA private keyoperation. Cryptographic units support the authentication ofcommunications from interacting agents, as well as allowing foranonymous transactions. Cryptographic units may also be configured aspart of the CPU. Equivalent microcontrollers and/or processors may alsobe used. Other commercially available specialized cryptographicprocessors include: Broadcom's CryptoNetX and other Security Processors;nCipher's nShield; SafeNet's Luna PCI (e.g., 7100) series; SemaphoreCommunications' 40 MHz Roadrunner 184; Sun's Cryptographic Accelerators(e.g., Accelerator 6000 PCIe Board, Accelerator 500 Daughtercard); ViaNano Processor (e.g., L2100, L2200, U2400) line, which is capable ofperforming 500+ MB/s of cryptographic instructions; VLSI Technology's 33MHz 6868; and/or the like.

Memory

Generally, any mechanization and/or embodiment allowing a processor toaffect the storage and/or retrieval of information is regarded as memory1329. However, memory is a fungible technology and resource, thus, anynumber of memory embodiments may be employed in lieu of or in concertwith one another. It is to be understood that the IDAP controller and/ora computer systemization may employ various forms of memory 1329. Forexample, a computer systemization may be configured wherein theoperation of on-chip CPU memory (e.g., registers), RAM, ROM, and anyother storage devices are provided by a paper punch tape or paper punchcard mechanism; however, such an embodiment would result in an extremelyslow rate of operation. In a typical configuration, memory 1329 willinclude ROM 1306, RAM 1305, and a storage device 1314. A storage device1314 may be any conventional computer system storage. Storage devicesmay include a drum; a (fixed and/or removable) magnetic disk drive; amagneto-optical drive; an optical drive (i.e., Blueray, CDROM/RAM/Recordable (R)/ReWritable (RW), DVD R/RW, HD DVD R/RW etc.); anarray of devices (e.g., Redundant Array of Independent Disks (RAID));solid state memory devices (USB memory, solid state drives (SSD), etc.);other processor-readable storage mediums; and/or other devices of thelike. Thus, a computer systemization generally requires and makes use ofmemory.

Component Collection

The memory 1329 may contain a collection of program and/or databasecomponents and/or data such as, but not limited to: operating systemcomponent(s) 1315 (operating system); information server component(s)1316 (information server); user interface component(s) 1317 (userinterface); Web browser component(s) 1318 (Web browser); database(s)1319; mail server component(s) 1321; mail client component(s) 1322;cryptographic server component(s) 1320 (cryptographic server); the IDAPcomponent(s) 1335; and/or the like (i.e., collectively a componentcollection). These components may be stored and accessed from thestorage devices and/or from storage devices accessible through aninterface bus. Although non-conventional program components such asthose in the component collection, typically, are stored in a localstorage device 1314, they may also be loaded and/or stored in memorysuch as: peripheral devices, RAM, remote storage facilities through acommunications network, ROM, various forms of memory, and/or the like.

Operating System

The operating system component 1315 is an executable program componentfacilitating the operation of the IDAP controller. Typically, theoperating system facilitates access of I/O, network interfaces,peripheral devices, storage devices, and/or the like. The operatingsystem may be a highly fault tolerant, scalable, and secure system suchas: Apple Macintosh OS X (Server); AT&T Plan 9; Be OS; Unix andUnix-like system distributions (such as AT&T's UNIX; Berkley SoftwareDistribution (BSD) variations such as FreeBSD, NetBSD, OpenBSD, and/orthe like; Linux distributions such as Red Hat, Ubuntu, and/or the like);and/or the like operating systems. However, more limited and/or lesssecure operating systems also may be employed such as Apple MacintoshOS, IBM OS/2, Microsoft DOS, Microsoft Windows2000/2003/3.1/95/98/CE/Millennium/NT/Vista/XP (Server), Palm OS, and/orthe like. An operating system may communicate to and/or with othercomponents in a component collection, including itself, and/or the like.Most frequently, the operating system communicates with other programcomponents, user interfaces, and/or the like. For example, the operatingsystem may contain, communicate, generate, obtain, and/or provideprogram component, system, user, and/or data communications, requests,and/or responses. The operating system, once executed by the CPU, mayenable the interaction with communications networks, data, I/O,peripheral devices, program components, memory, user input devices,and/or the like. The operating system may provide communicationsprotocols that allow the IDAP controller to communicate with otherentities through a communications network 1313. Various communicationprotocols may be used by the IDAP controller as a subcarrier transportmechanism for interaction, such as, but not limited to: multicast,TCP/IP, UDP, unicast, and/or the like.

Information Server

An information server component 1316 is a stored program component thatis executed by a CPU. The information server may be a conventionalInternet information server such as, but not limited to Apache SoftwareFoundation's Apache, Microsoft's Internet Information Server, and/or thelike. The information server may allow for the execution of programcomponents through facilities such as Active Server Page (ASP), ActiveX,(ANSI) (Objective-) C (++), C# and/or .NET, Common Gateway Interface(CGI) scripts, dynamic (D) hypertext markup language (HTML), FLASH,Java, JavaScript, Practical Extraction Report Language (PERL), HypertextPre-Processor (PHP), pipes, Python, wireless application protocol (WAP),WebObjects, and/or the like. The information server may support securecommunications protocols such as, but not limited to, File TransferProtocol (FTP); HyperText Transfer Protocol (HTTP); Secure HypertextTransfer Protocol (HTTPS), Secure Socket Layer (SSL), messagingprotocols (e.g., America Online (AOL) Instant Messenger (AIM),Application Exchange (APEX), ICQ, Internet Relay Chat (IRC), MicrosoftNetwork (MSN) Messenger Service, Presence and Instant Messaging Protocol(PRIM), Internet Engineering Task Force's (IETF's) Session InitiationProtocol (SIP), SIP for Instant Messaging and Presence LeveragingExtensions (SIMPLE), open XML-based Extensible Messaging and PresenceProtocol (XMPP) (i.e., Jabber or Open Mobile Alliance's (OMA's) InstantMessaging and Presence Service (IMPS)), Yahoo! Instant MessengerService, and/or the like. The information server provides results in theform of Web pages to Web browsers, and allows for the manipulatedgeneration of the Web pages through interaction with other programcomponents. After a Domain Name System (DNS) resolution portion of anHTTP request is resolved to a particular information server, theinformation server resolves requests for information at specifiedlocations on the IDAP controller based on the remainder of the HTTPrequest. For example, a request such ashttp://123.124.125.126/myInformation.html might have the IP portion ofthe request “123.124.125.126” resolved by a DNS server to an informationserver at that IP address; that information server might in turn furtherparse the http request for the “/myInformation.html” portion of therequest and resolve it to a location in memory containing theinformation “myInformation.html.” Additionally, other informationserving protocols may be employed across various ports, e.g., FTPcommunications across port 21, and/or the like. An information servermay communicate to and/or with other components in a componentcollection, including itself, and/or facilities of the like. Mostfrequently, the information server communicates with the IDAP database1319, operating systems, other program components, user interfaces, Webbrowsers, and/or the like.

Access to the IDAP database may be achieved through a number of databasebridge mechanisms such as through scripting languages as enumeratedbelow (e.g., CGI) and through inter-application communication channelsas enumerated below (e.g., CORBA, WebObjects, etc.). Any data requeststhrough a Web browser are parsed through the bridge mechanism intoappropriate grammars as required by the IDAP. In one embodiment, theinformation server would provide a Web form accessible by a Web browser.Entries made into supplied fields in the Web form are tagged as havingbeen entered into the particular fields, and parsed as such. The enteredterms are then passed along with the field tags, which act to instructthe parser to generate queries directed to appropriate tables and/orfields. In one embodiment, the parser may generate queries in standardSQL by instantiating a search string with the proper join/selectcommands based on the tagged text entries, wherein the resulting commandis provided over the bridge mechanism to the IDAP as a query. Upongenerating query results from the query, the results are passed over thebridge mechanism, and may be parsed for formatting and generation of anew results Web page by the bridge mechanism. Such a new results Webpage is then provided to the information server, which may supply it tothe requesting Web browser.

Also, an information server may contain, communicate, generate, obtain,and/or provide program component, system, user, and/or datacommunications, requests, and/or responses.

User Interface

Computer interfaces in some respects are similar to automobile operationinterfaces. Automobile operation interface elements such as steeringwheels, gearshifts, and speedometers facilitate the access, operation,and display of automobile resources, and status. Computer interactioninterface elements such as check boxes, cursors, menus, scrollers, andwindows (collectively and commonly referred to as widgets) similarlyfacilitate the access, capabilities, operation, and display of data andcomputer hardware and operating system resources, and status. Operationinterfaces are commonly called user interfaces. Graphical userinterfaces (GUIs) such as the Apple Macintosh Operating System's Aqua,IBM's OS/2, Microsoft's Windows2000/2003/3.1/95/98/CE/Millennium/NT/XP/Vista/7 (i.e., Aero), Unix'sX-Windows (e.g., which may include additional Unix graphic interfacelibraries and layers such as K Desktop Environment (KDE), mythTV and GNUNetwork Object Model Environment (GNOME)), web interface libraries(e.g., ActiveX, AJAX, (D)HTML, FLASH, Java, JavaScript, etc. interfacelibraries such as, but not limited to, Dojo, jQuery(UI), MooTools,Prototype, script.aculo.us, SWFObject, Yahoo! User Interface, any ofwhich may be used and) provide a baseline and means of accessing anddisplaying information graphically to users.

A user interface component 1317 is a stored program component that isexecuted by a CPU. The user interface may be a conventional graphic userinterface as provided by, with, and/or atop operating systems and/oroperating environments such as already discussed. The user interface mayallow for the display, execution, interaction, manipulation, and/oroperation of program components and/or system facilities through textualand/or graphical facilities. The user interface provides a facilitythrough which users may affect, interact, and/or operate a computersystem. A user interface may communicate to and/or with other componentsin a component collection, including itself, and/or facilities of thelike. Most frequently, the user interface communicates with operatingsystems, other program components, and/or the like. The user interfacemay contain, communicate, generate, obtain, and/or provide programcomponent, system, user, and/or data communications, requests, and/orresponses.

Web Browser

A Web browser component 1318 is a stored program component that isexecuted by a CPU. The Web browser may be a conventional hypertextviewing application such as Microsoft Internet Explorer or NetscapeNavigator. Secure Web browsing may be supplied with 128 bit (or greater)encryption by way of HTTPS, SSL, and/or the like. Web browsers allowingfor the execution of program components through facilities such asActiveX, AJAX, (D)HTML, FLASH, Java, JavaScript, web browser plug-inAPIs (e.g., FireFox, Safari Plug-in, and/or the like APIs), and/or thelike. Web browsers and like information access tools may be integratedinto PDAs, cellular telephones, and/or other mobile devices. A Webbrowser may communicate to and/or with other components in a componentcollection, including itself, and/or facilities of the like. Mostfrequently, the Web browser communicates with information servers,operating systems, integrated program components (e.g., plug-ins),and/or the like; e.g., it may contain, communicate, generate, obtain,and/or provide program component, system, user, and/or datacommunications, requests, and/or responses. Also, in place of a Webbrowser and information server, a combined application may be developedto perform similar operations of both. The combined application wouldsimilarly affect the obtaining and the provision of information tousers, user agents, and/or the like from the IDAP enabled nodes. Thecombined application may be nugatory on systems employing standard Webbrowsers.

Mail Server

A mail server component 1321 is a stored program component that isexecuted by a CPU 1303. The mail server may be a conventional Internetmail server such as, but not limited to sendmail, Microsoft Exchange,and/or the like. The mail server may allow for the execution of programcomponents through facilities such as ASP, ActiveX, (ANSI) (Objective-)C (++), C# and/or .NET, CGI scripts, Java, JavaScript, PERL, PHP, pipes,Python, WebObjects, and/or the like. The mail server may supportcommunications protocols such as, but not limited to: Internet messageaccess protocol (IMAP), Messaging Application Programming Interface(MAPI)/Microsoft Exchange, post office protocol (POP3), simple mailtransfer protocol (SMTP), and/or the like. The mail server can route,forward, and process incoming and outgoing mail messages that have beensent, relayed and/or otherwise traversing through and/or to the IDAP.

Access to the IDAP mail may be achieved through a number of APIs offeredby the individual Web server components and/or the operating system.

Also, a mail server may contain, communicate, generate, obtain, and/orprovide program component, system, user, and/or data communications,requests, information, and/or responses.

Mail Client

A mail client component 1322 is a stored program component that isexecuted by a CPU 1303. The mail client may be a conventional mailviewing application such as Apple Mail, Microsoft Entourage, MicrosoftOutlook, Microsoft Outlook Express, Mozilla, Thunderbird, and/or thelike. Mail clients may support a number of transfer protocols, such as:IMAP, Microsoft Exchange, POP3, SMTP, and/or the like. A mail client maycommunicate to and/or with other components in a component collection,including itself, and/or facilities of the like. Most frequently, themail client communicates with mail servers, operating systems, othermail clients, and/or the like; e.g., it may contain, communicate,generate, obtain, and/or provide program component, system, user, and/ordata communications, requests, information, and/or responses. Generally,the mail client provides a facility to compose and transmit electronicmail messages.

Cryptographic Server

A cryptographic server component 1320 is a stored program component thatis executed by a CPU 1303, cryptographic processor 1326, cryptographicprocessor interface 1327, cryptographic processor device 1328, and/orthe like. Cryptographic processor interfaces will allow for expeditionof encryption and/or decryption requests by the cryptographic component;however, the cryptographic component, alternatively, may run on aconventional CPU. The cryptographic component allows for the encryptionand/or decryption of provided data. The cryptographic component allowsfor both symmetric and asymmetric (e.g., Pretty Good Protection (PGP))encryption and/or decryption. The cryptographic component may employcryptographic techniques such as, but not limited to: digitalcertificates (e.g., X.509 authentication framework), digital signatures,dual signatures, enveloping, password access protection, public keymanagement, and/or the like. The cryptographic component will facilitatenumerous (encryption and/or decryption) security protocols such as, butnot limited to: checksum, Data Encryption Standard (DES), EllipticalCurve Encryption (ECC), International Data Encryption Algorithm (IDEA),Message Digest 5 (MD5, which is a one way hash operation), passwords,Rivest Cipher (RC5), Rijndael, RSA (which is an Internet encryption andauthentication system that uses an algorithm developed in 1977 by RonRivest, Adi Shamir, and Leonard Adleman), Secure Hash Algorithm (SHA),Secure Socket Layer (SSL), Secure Hypertext Transfer Protocol (HTTPS),and/or the like. Employing such encryption security protocols, the IDAPmay encrypt all incoming and/or outgoing communications and may serve asnode within a virtual private network (VPN) with a wider communicationsnetwork. The cryptographic component facilitates the process of“security authorization” whereby access to a resource is inhibited by asecurity protocol wherein the cryptographic component effects authorizedaccess to the secured resource. In addition, the cryptographic componentmay provide unique identifiers of content, e.g., employing and MD5 hashto obtain a unique signature for an digital audio file. A cryptographiccomponent may communicate to and/or with other components in a componentcollection, including itself, and/or facilities of the like. Thecryptographic component supports encryption schemes allowing for thesecure transmission of information across a communications network toenable the IDAP component to engage in secure transactions if sodesired. The cryptographic component facilitates the secure accessing ofresources on the IDAP and facilitates the access of secured resources onremote systems; i.e., it may act as a client and/or server of securedresources. Most frequently, the cryptographic component communicateswith information servers, operating systems, other program components,and/or the like. The cryptographic component may contain, communicate,generate, obtain, and/or provide program component, system, user, and/ordata communications, requests, and/or responses.

The IDAP Database

The IDAP database component 1319 may be embodied in a database and itsstored data. The database is a stored program component, which isexecuted by the CPU; the stored program component portion configuringthe CPU to process the stored data. The database may be a conventional,fault tolerant, relational, scalable, secure database such as Oracle orSybase. Relational databases are an extension of a flat file. Relationaldatabases consist of a series of related tables. The tables areinterconnected via a key field. Use of the key field allows thecombination of the tables by indexing against the key field; i.e., thekey fields act as dimensional pivot points for combining informationfrom various tables. Relationships generally identify links maintainedbetween tables by matching primary keys. Primary keys represent fieldsthat uniquely identify the rows of a table in a relational database.More precisely, they uniquely identify rows of a table on the “one” sideof a one-to-many relationship.

Alternatively, the IDAP database may be implemented using variousstandard data-structures, such as an array, hash, (linked) list, struct,structured text file (e.g., XML), table, and/or the like. Suchdata-structures may be stored in memory and/or in (structured) files. Inanother alternative, an object-oriented database may be used, such asFrontier, ObjectStore, Poet, Zope, and/or the like. Object databases caninclude a number of object collections that are grouped and/or linkedtogether by common attributes; they may be related to other objectcollections by some common attributes. Object-oriented databases performsimilarly to relational databases with the exception that objects arenot just pieces of data but may have other types of capabilitiesencapsulated within a given object. If the IDAP database is implementedas a data-structure, the use of the IDAP database 1319 may be integratedinto another component such as the IDAP component 1335. Also, thedatabase may be implemented as a mix of data structures, objects, andrelational structures. Databases may be consolidated and/or distributedin countless variations through standard data processing techniques.Portions of databases, e.g., tables, may be exported and/or imported andthus decentralized and/or integrated.

In one embodiment, the database component 1319 includes several tables1319 a-l. A Users table 1319 a may include fields such as, but notlimited to: user_ID, name, login, password, contact_info, query-history,settings, preferences, primal_ID(s), insight_ID(s), report_ID(s), and/orthe like. The user table may support and/or track multiple entityaccounts on a IDAP. An Index table 1319 b may include fields such as,but not limited to: index_ID, index_type, data_feed_ID(s),industry_ID(s), term(s), data_type(s), data_type_value(s), snippet(s),source(s), author(s), date(s), and/or the like. A Raw Data table 1319 cmay include fields such as, but not limited to: raw_data_ID,data_feed_ID(s), index_ID(s), compacted_data_ID(s), raw_data_type,raw_data_content, fields, raw_data_parameters, and/or the like. ACompacted Data table 1319 d may include fields such as, but not limitedto: compacted_data_ID, data_feed_ID(s), index_ID(s), raw_data (ID),raw_data_type, compacted_data_content, fields,compacted_data_parameters, and/or the like. In one implementation, thedata feed may be populated by a social media data feed (e.g., Facebookstatus updates, Twitter feed, and/or the like), by a market data feed(e.g., Bloomberg's PhatPipe, Dun & Bradstreet, Reuter'sTib, Triarch,etc.), and/or the like, such as, for example, through Microsoft's ActiveTemplate Library and Dealing Object Technology's real-time toolkitRtt.Multi. A Queries table 1319 e may include fields such as, but notlimited to: query ID, query_type, query_configuration, query_content,fields, precision, recall, user_ID(s), raw_data_ID(s),compacted_data_ID(s), and/or the like. A Primals table 1319 f mayinclude fields such as, but not limited to: primal_ID, topic,demographic, time, brand, nationality, group_association,primal_data_value, and/or the like. An Insights table 1319 g may includefields such as, but not limited to: insight_ID, primal_ID(s),user_ID(s), insight_template, insight_natural_language_template,filters, ranking, tiering, thresholds, and/or the like. A Reports table1319 h may include fields such as, but not limited to: report_ID,report_name, user_ID(s), primal_ID(s), insight_ID(s), report_template,topic_ID(s), topic_set ID(s), intent_driver_ID(s), campaign_ID(s),and/or the like. A Topics table 1319 i may include fields such as, butnot limited to: topic_ID, query_term, related_term(s), query_ID(s),primal_ID(s), user_ID(s), and/or the like. A Topic Sets table 1319 j mayinclude fields such as, but not limited to: topic_set_ID, topic_ID(s),query_ID(s), queries, precision, recall, user_ID(s), and/or the like. AnIntent Drivers table 1319 k may include fields such as, but not limitedto: intent_driver_ID, term, phrase, image, sound, action, behavior,pattern, time, global_metric(s), correlation(s), primal_ID(s),user_ID(s), insight_ID(s) and/or the like. A Campaigns table 1319 l mayinclude fields such as, but not limited to: campaign_ID,intent_driver_ID(s), efficacy, return_on_investment, test_group(s),control_group(s), user_ID(s), and/or the like.

In one embodiment, the IDAP database may interact with other databasesystems. For example, employing a distributed database system, queriesand data access by search IDAP component may treat the combination ofthe IDAP database, an integrated data security layer database as asingle database entity.

In one embodiment, user programs may contain various user interfaceprimitives, which may serve to update the IDAP. Also, various accountsmay require custom database tables depending upon the environments andthe types of clients the IDAP may need to serve. It should be noted thatany unique fields may be designated as a key field throughout. In analternative embodiment, these tables have been decentralized into theirown databases and their respective database controllers (i.e.,individual database controllers for each of the above tables). Employingstandard data processing techniques, one may further distribute thedatabases over several computer systemizations and/or storage devices.Similarly, configurations of the decentralized database controllers maybe varied by consolidating and/or distributing the various databasecomponents 1319 a-l. The IDAP may be configured to keep track of varioussettings, inputs, and parameters via database controllers.

The IDAP database may communicate to and/or with other components in acomponent collection, including itself, and/or facilities of the like.Most frequently, the IDAP database communicates with the IDAP component,other program components, and/or the like. The database may contain,retain, and provide information regarding other nodes and data.

The IDAPs

The IDAP component 1335 is a stored program component that is executedby a CPU. In one embodiment, the IDAP component incorporates any and/orall combinations of the aspects of the IDAP that was discussed in theprevious figures. As such, the IDAP affects accessing, obtaining and theprovision of information, services, transactions, and/or the like acrossvarious communications networks. The features and embodiments of theIDAP discussed herein increase network efficiency by reducing datatransfer requirements the use of more efficient data structures andmechanisms for their transfer and storage. As a consequence, more datamay be transferred in less time, and latencies with regard totransactions, are also reduced. In many cases, such reduction instorage, transfer time, bandwidth requirements, latencies, etc., willreduce the capacity and structural infrastructure requirements tosupport the IDAP's features and facilities, and in many cases reduce thecosts, energy consumption/requirements, and extend the life of IDAP'sunderlying infrastructure; this has the added benefit of making the IDAPmore reliable. Similarly, many of the features and mechanisms aredesigned to be easier for users to use and access, thereby broadeningthe audience that may enjoy/employ and exploit the feature sets of theIDAP; such ease of use also helps to increase the reliability of theIDAP. In addition, the feature sets include heightened security as notedvia the Cryptographic components 1320, 1326, 1328 and throughout, makingaccess to the features and data more reliable and secure

The IDAP transforms raw data, query, and, UI interaction inputs via IDAPQuery Processing 1341, Faceted Search 1342, Record Compacting 1343, andField Selecting 1344, Primal Evaluation 1345, Insight Generation 1346,Filtering/Ranking 1347, Report Generation Query Optimization 1349, andInfluence Discovery 1350 components into query result outputs, topics,topic sets, intent drivers, campaign efficacy metrics, and/or the like.

The IDAP component enabling access of information between nodes may bedeveloped by employing standard development tools and languages such as,but not limited to: Apache components, Assembly, ActiveX, binaryexecutables, (ANSI) (Objective-) C (++), C# and/or .NET, databaseadapters, CGI scripts, Java, JavaScript, mapping tools, procedural andobject oriented development tools, PERL, PHP, Python, shell scripts, SQLcommands, web application server extensions, web developmentenvironments and libraries (e.g., Microsoft's ActiveX; Adobe AIR, FLEX &FLASH; AJAX; (D)HTML; Dojo, Java; JavaScript; jQuery(UI); MooTools;Prototype; script.aculo.us; Simple Object Access Protocol (SOAP);SWFObject; Yahoo! User Interface; and/or the like), WebObjects, and/orthe like. In one embodiment, the IDAP server employs a cryptographicserver to encrypt and decrypt communications. The IDAP component maycommunicate to and/or with other components in a component collection,including itself, and/or facilities of the like. Most frequently, theIDAP component communicates with the IDAP database, operating systems,other program components, and/or the like. The IDAP may contain,communicate, generate, obtain, and/or provide program component, system,user, and/or data communications, requests, and/or responses.

Distributed IDAPs

The structure and/or operation of any of the IDAP node controllercomponents may be combined, consolidated, and/or distributed in anynumber of ways to facilitate development and/or deployment. Similarly,the component collection may be combined in any number of ways tofacilitate deployment and/or development. To accomplish this, one mayintegrate the components into a common code base or in a facility thatcan dynamically load the components on demand in an integrated fashion.

The component collection may be consolidated and/or distributed incountless variations through standard data processing and/or developmenttechniques. Multiple instances of any one of the program components inthe program component collection may be instantiated on a single node,and/or across numerous nodes to improve performance throughload-balancing and/or data-processing techniques. Furthermore, singleinstances may also be distributed across multiple controllers and/orstorage devices; e.g., databases. All program component instances andcontrollers working in concert may do so through standard dataprocessing communication techniques.

The configuration of the IDAP controller will depend on the context ofsystem deployment. Factors such as, but not limited to, the budget,capacity, location, and/or use of the underlying hardware resources mayaffect deployment requirements and configuration. Regardless of if theconfiguration results in more consolidated and/or integrated programcomponents, results in a more distributed series of program components,and/or results in some combination between a consolidated anddistributed configuration, data may be communicated, obtained, and/orprovided. Instances of components consolidated into a common code basefrom the program component collection may communicate, obtain, and/orprovide data. This may be accomplished through intra-application dataprocessing communication techniques such as, but not limited to: datareferencing (e.g., pointers), internal messaging, object instancevariable communication, shared memory space, variable passing, and/orthe like.

If component collection components are discrete, separate, and/orexternal to one another, then communicating, obtaining, and/or providingdata with and/or to other component components may be accomplishedthrough inter-application data processing communication techniques suchas, but not limited to: Application Program Interfaces (API) informationpassage; (distributed) Component Object Model ((D)COM), (Distributed)Object Linking and Embedding ((D)OLE), and/or the like), Common ObjectRequest Broker Architecture (CORBA), Jini local and remote applicationprogram interfaces, JavaScript Object Notation (JSON), Remote MethodInvocation (RMI), SOAP, process pipes, shared files, and/or the like.Messages sent between discrete component components forinter-application communication or within memory spaces of a singularcomponent for intra-application communication may be facilitated throughthe creation and parsing of a grammar. A grammar may be developed byusing development tools such as lex, yacc, XML, and/or the like, whichallow for grammar generation and parsing capabilities, which in turn mayform the basis of communication messages within and between components.

For example, a grammar may be arranged to recognize the tokens of anHTTP post command, e.g.:

-   -   w3c -post http:// . . . Value1

where Value1 is discerned as being a parameter because “http://” is partof the grammar syntax, and what follows is considered part of the postvalue. Similarly, with such a grammar, a variable “Value1” may beinserted into an “http://” post command and then sent. The grammarsyntax itself may be presented as structured data that is interpretedand/or otherwise used to generate the parsing mechanism (e.g., a syntaxdescription text file as processed by lex, yacc, etc.). Also, once theparsing mechanism is generated and/or instantiated, it itself mayprocess and/or parse structured data such as, but not limited to:character (e.g., tab) delineated text, HTML, structured text streams,XML, and/or the like structured data. In another embodiment,inter-application data processing protocols themselves may haveintegrated and/or readily available parsers (e.g., JSON, SOAP, and/orlike parsers) that may be employed to parse (e.g., communications) data.Further, the parsing grammar may be used beyond message parsing, but mayalso be used to parse: databases, data collections, data stores,structured data, and/or the like. Again, the desired configuration willdepend upon the context, environment, and requirements of systemdeployment.

For example, in some implementations, the IDAP controller may beexecuting a PHP script implementing a Secure Sockets Layer (“SSL”)socket server via the information server, which listens to incomingcommunications on a server port to which a client may send data, e.g.,data encoded in JSON format. Upon identifying an incoming communication,the PHP script may read the incoming message from the client device,parse the received JSON-encoded text data to extract information fromthe JSON-encoded text data into PHP script variables, and store the data(e.g., client identifying information, etc.) and/or extractedinformation in a relational database accessible using the StructuredQuery Language (“SQL”). An exemplary listing, written substantially inthe form of PHP/SQL commands, to accept JSON-encoded input data from aclient device via a SSL connection, parse the data to extract variables,and store the data to a database, is provided below:

<?PHP header(‘Content-Type: text/plain’); // set ip address and port tolisten to for incoming data $address = ‘192.168.0.100’; $port = 255; //create a server-side SSL socket, listen for/accept incomingcommunication $sock = socket_create(AF_INET, SOCK_STREAM, 0);socket_bind($sock, $address, $port) or die(‘Could not bind to address’);socket_listen($sock); $client = socket_accept($sock); // read input datafrom client device in 1024 byte blocks until end of message do {         $input = “”;          $input = socket_read($client, 1024);         $data .= $input; } while($input != “”); // parse data toextract variables $obj = json_decode($data, true); // store input datain a database mysql_connect(″201.408.185.132″,$DBserver,$password); //access database server mysql_select(″CLIENT_DB.SQL″); // select databaseto append mysql_query(“INSERT INTO UserTable (transmission) VALUES($data)”); // add data to UserTable table in a CLIENT databasemysql_close(″CLIENT_DB.SQL″); // close connection to database ?>

Also, the following resources may be used to provide example embodimentsregarding SOAP parser implementation:

-   -   http://www.xay.com/perl/site/lib/SOAP/Parser.html    -   http://publib.boulder.ibm.com/infocenter/tivihelp/v2rl/index.jsp?topic=/com.ibm.IBMDI.doc/referenceguide295.htm        and other parser implementations:    -   http://publib.boulder.ibm.com/infocenter/tivihelp/v2rl/index.jsp?topic=/com.ibm.IBMDI.doc/referenceguide259.htm        all of which are hereby expressly incorporated by reference.

In order to address various issues and advance the art, the entirety ofthis application for APPARATUSES, METHODS AND SYSTEMS FOR INSIGHTDISCOVERY AND PRESENTATION FROM STRUCTURED AND UNSTRUCTURED DATA(including the Cover Page, Title, Headings, Field, Background, Summary,Brief Description of the Drawings, Detailed Description, Claims,Abstract, Figures, Appendices, and otherwise) shows, by way ofillustration, various embodiments in which the claimed innovations maybe practiced. The advantages and features of the application are of arepresentative sample of embodiments only, and are not exhaustive and/orexclusive. They are presented only to assist in understanding and teachthe claimed principles. It should be understood that they are notrepresentative of all claimed innovations. As such, certain aspects ofthe disclosure have not been discussed herein. That alternateembodiments may not have been presented for a specific portion of theinnovations or that further undescribed alternate embodiments may beavailable for a portion is not to be considered a disclaimer of thosealternate embodiments. It will be appreciated that many of thoseundescribed embodiments incorporate the same principles of theinnovations and others are equivalent. Thus, it is to be understood thatother embodiments may be utilized and functional, logical, operational,organizational, structural and/or topological modifications may be madewithout departing from the scope and/or spirit of the disclosure. Assuch, all examples and/or embodiments are deemed to be non-limitingthroughout this disclosure. Also, no inference should be drawn regardingthose embodiments discussed herein relative to those not discussedherein other than it is as such for purposes of reducing space andrepetition. For instance, it is to be understood that the logical and/ortopological structure of any combination of any program components (acomponent collection), other components and/or any present feature setsas described in the figures and/or throughout are not limited to a fixedoperating order and/or arrangement, but rather, any disclosed order isexemplary and all equivalents, regardless of order, are contemplated bythe disclosure. Furthermore, it is to be understood that such featuresare not limited to serial execution, but rather, any number of threads,processes, services, servers, and/or the like that may executeasynchronously, concurrently, in parallel, simultaneously,synchronously, and/or the like are contemplated by the disclosure. Assuch, some of these features may be mutually contradictory, in that theycannot be simultaneously present in a single embodiment. Similarly, somefeatures are applicable to one aspect of the innovations, andinapplicable to others. In addition, the disclosure includes otherinnovations not presently claimed. Applicant reserves all rights inthose presently unclaimed innovations including the right to claim suchinnovations, file additional applications, continuations, continuationsin part, divisions, and/or the like thereof. As such, it should beunderstood that advantages, embodiments, examples, functional, features,logical, operational, organizational, structural, topological, and/orother aspects of the disclosure are not to be considered limitations onthe disclosure as defined by the claims or limitations on equivalents tothe claims.

What is claimed is:
 1. A processor-implemented method, comprising:identifying primal data variables, including at least topic, demographicand brand; determining primal data variable trends over time; generatinga plurality of insight records based on a relationship within a corpusof documents between the primal data variable trends for at least twoprimal data variables; filtering the insight records according topre-filtering criteria; sorting the insight records according to rankingcriteria; identifying at least one insight natural language presentationtemplate associated in memory with at least one of the plurality ofinsight records, the at least one insight natural language presentationtemplate configured to generate a natural language report comprising therelationship between the primal data variable trends for the at leasttwo primal data variables; populating the at least one natural languagepresentation template with the at least two primal data variables andthe primal data variable trends to yield the natural language report;and providing the natural language report for display.
 2. The method ofclaim 1, wherein the primal data variables are identified from packedrecords stored in one or more worker nodes in a distributed storageenvironment.
 3. The method of claim 2, wherein the distributed storageenvironment comprises an akka cluster.
 4. The method of claim 1, whereinthe corpus of documents comprises at least one social media data feed.5. The method of claim 1, wherein determining primal data variabletrends over time further comprises determining a change in correlationbetween at the least two primal data variables between two points intime.
 6. The method of claim 1, wherein filtering the insight recordsaccording to pre-filtering criteria comprises applying at least oneblacklist filter.
 7. The method of claim 1, wherein filtering theinsight records according to pre-filtering criteria comprises applyingat least one colinearity filter.
 8. The method of claim 1, whereinfiltering the insight records according to pre-filtering criteriacomprises applying at least one confidence filter.
 9. The method ofclaim 1, wherein sorting the insight records according to rankingcriteria further comprises: determining an insight rating score based onthe ranking criteria for each of the plurality of insight records;comparing the insight rating score to at least one tiering threshold foreach of the plurality of insight records; and associating each of theplurality of insight records with a tier based on the comparing.
 10. Themethod of claim 1, wherein the ranking criteria includes at least one ofa degree of change of a primal value relationship, a direction of changeof a primal value relationship, an insight actionability, and anexpected return-on-investment.
 11. The method of claim 1, wherein thenatural language report is persisted in at least one data recordassociated with at least one of the plurality of insight records. 12.The method of claim 1, wherein the natural language report is persistedin at least one data record associated with at least one user account.13. The method of claim 1, further comprising: providing the pluralityof insight records for display; and receiving at least one curationinput in response to the display of the plurality of insight records.14. The method of claim 13, wherein the at least one curation inputincludes at least one of an assignment of at least one insight record toa tier, adjustment of measures, strength scores, and strengthclassifications.
 15. A system, comprising: a processor; a memorydisposed in communication with the processor and storing programinstructions that, when executed by the processor, cause the processorto: identify primal data variables, including at least topic,demographic and brand; determine primal data variable trends over time;generate a plurality of insight records based on a relationship within acorpus of documents between the primal data variable trends for at leasttwo primal data variables; filter the insight records according topre-filtering criteria; sort the insight records according to rankingcriteria; identify at least one insight natural language presentationtemplate associated in memory with at least one of the plurality ofinsight records, the at least one natural language presentation templateconfigured to generate a natural language report comprising therelationship between the primal data variable trends for the at leasttwo primal data variables; populate the at least one natural languagepresentation template with the at least two primal data variables andthe primal data variable trends to yield a natural language report; andprovide the natural language report for display.
 16. Aprocessor-accessible non-transitory medium storing processor-issuableinstructions, comprising: identify primal data variables, including atleast topic, demographic and brand; determine primal data variabletrends over time; generate a plurality of insight records based on arelationship within a corpus of documents between the primal datavariable trends for at least two primal data variables; filter theinsight records according to pre-filtering criteria; sort the insightrecords according to ranking criteria; identify at least one insightnatural language presentation template associated in memory with atleast one of the plurality of insight records, the at least one insightnatural language presentation template configured to generate a naturallanguage report comprising the relationship between the primal datavariable trends for the at least two primal data variables; populate theat least one natural language presentation template with the at leasttwo primal data variables and the primal data variable trends to yield anatural language report; and provide the natural language report fordisplay.
 17. The method of claim 10, wherein the ranking criteriaincludes the insight actionability, and further comprising: calculatinga primal elasticity associated with at least one of the primal datavariables; and determining the insight actionability based on the primalelasticity.
 18. The method of claim 1, wherein generating a plurality ofinsight records and sorting the insight records are performedperiodically according to at least one user-customized period.
 19. Themethod of claim 1, wherein the natural language report is presented viaa web page.